In this episode, Abate flew to Denver, Colorado, to get a behind-the-scenes look at the future of recycling with Joe Castagneri, the head of AI at Amp Robotics. With Materials Recovery Facilities (MRFs) processing a staggering 25 tons of trash per hour, robotic sorting is the clear long-term solution.

Recycling is a for-profit industry. When the margins don’t make sense, the items will not be recycled. This is why Amp’s mission to use robotics and AI to bring down the cost of recycling and increase the number of items that can be sorted for recycling is so impactful.

Joe Castagneri
Joe Castagneri graduated with his Master of Science in Applied Mathematics, with an undergrad degree in Physics. While still in university, he first joined the team at Amp Robotics in 2016 where he worked on Machine Learning models to identify recyclables in video streams of Trash in Materials Recovery Facilities (MRFs). Today, he is the Head of AI at Amp Robotics where he is changing the economics of recycling through automation.

Conventional sheet metal manufacturing is highly inefficient for the low-volume production seen in the space industry. At Machina Labs, they developed a novel method of forming sheet metal using two robotic arms to bend the metal into different geometries. This method cuts down the time to produce large sheet metal parts from several months down to a few hours. Ed Mehr, Co-Founder and CEO of Machina Labs, explains this revolutionary manufacturing process.

Ed Mehr

Ed Mehr is the co-founder and CEO of Machina Labs. He has an engineering background in smart manufacturing and artificial intelligence. In his previous position at Relativity Space, he led a team in charge of developing the world’s largest metal 3D printer. Relativity Space uses 3D printing to make rocket parts rapidly, and with the flexibility for multiple iterations. Ed previously was the CTO at Cloudwear (Now Averon), and has also worked at SpaceX, Google, and Microsoft.

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The Recycling Industry in the United States is a for-profit industry. They profit from taking recyclable material, refining it, and reselling it to companies at a cheaper price than producing the material from scratch.

Supply and Demand in Recycling

If you look at the demand side of the recycling industry, an array of multi-billion dollar companies like Coca-Cola and PepsiCo are incentivized to buy recycled goods and reduce their materials costs.

On the supply side, ~300 million tons of trash are generated annually in the United States. Estimates suggest that up to 75% of that is recyclable.

On paper, it seems clear that maximizing the amount of trash the US recycles is in everyone’s interest. One issue though, less than a third of the trash ends up recycled.

Areeb, co-founder of Glacier, breaks down the multi-layered reasoning behind why the Recycling industry cannot handle this volume of trash, and what Glacier is doing to address this.

Areeb Malik

Areeb Malik is the Co-Founder of Glacier, and he is on a personal mission to fight climate change and extract value from the $123B worth of recyclables that fill the landfills and oceans. Before founding Glacier, Areeb was a Software Engineer at Facebook, where he used Machine Learning and Computer Vision to build out new product features.

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At ICRA 2022, the researchers weren’t the only ones working with cutting-edge technology. We spoke to the exhibitors to get real-life demos of their products.

Tangram Vision

Tangram Vision is a hardware-agnostic sensor fusion platform. It streamlines the development and deployment of critical sensor infrastructure like calibration, fusion, and monitoring for any number of cameras, depth sensors, LiDAR, radar, and IMU. Their co-founder, Adam Rodnitzky, walks us through their sensor fusion platform.

FLX Solutions

Matt Bilsky, Founder and CEO of FLX Solutions gives us a live demo of their robot, the FLX BOT. Matt Bilsky applied his Ph.D. in Mechanical Engineering to create a novel, highly compact robot that is designed to reach and inspect parts of a building that a human cannot reach. The FLX BOT is one inch in diameter and is made of modular links that can be attached one after the other to extend the reach and degrees of freedom of the robot.

Exyn Technologies

Exyn Technologies specializes in aerial robotics, drone swarms, multi-modal sensor fusion, 3D mapping, obstacle avoidance, and autonomous navigation & planning.

Exyn’s focus is on developing software for aerial robots so they can operate in GPS-denied environments, without human control, prior information, or pre-existing infrastructure (e.g. no motion capture system).

Pollen Robotics

Pollen Robotics developed the robot, Reachy, an open-source humanoid robot with a quirky appearance. Reachy’s primary userbase is researchers studying fields such as teleoperation. Using mixed autonomy, Reachy is able to be teleoperated by humans, while still having onboard intelligence to autonomously infer the actions you want it to do.

Brandon Gilles, Founder and CEO of Luxonis, tells us his story about how Luxonis designed one of the most versatile perception platforms on the market.

Brandon took the lessons learned from his time at Ubiquiti, which transformed networking with network-on-a-chip architectures, and applied the mastery of embedded hardware and software to the OAK-D camera and the broader OAK line of products.

To refer to the OAK-D as a stereovision camera tells only part of the story. Aside from depth sensing, the OAK-D leverages the Intel Myriad X to perform perception computations directly on the camera in a highly power-efficient architecture.

Customers can also instantly leverage a wide array of open-source computer vision and AI packages that are pre-calibrated to the optics system.

Additionally, by leveraging a system-on-a-module design, the Luxonis team easily churns out a multitude of variations of the hardware platform to fit the wide variety of customer use cases. Tune in for more.

Brandon Gilles

Brandon Gilles is the Founder and CEO of Luxonis, maker of the OAK-D line of cameras. Brandon comes from a background in Electrical and RF Engineering. He spent his early career as a UniFi Lead at Ubiquiti, where his team helped bring Ubiquiti’s highly performant and power-efficient Unifi products to market.

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An internationally leading robotics initiative that enables academia and industry to find innovative solutions to real world challenges, celebrated its success with a Hot Robotics Symposium hosted across three UK regions last week.

The National Nuclear User Facility (NNUF) for Hot Robotics is a government funded initiative that supports innovation in the nuclear sector by making world-leading testing facilities, sensors and robotic equipment easily accessible to academia and industry.

Ground-breaking, impactful research in robotics and artificial intelligence will benefit the UK’s development of fusion energy as safe, low carbon and sustainable energy source in addition to adjacent sectors such as nuclear decommissioning, space, and mobile applications.

Visitors to UKAEA’s RACE (UK Atomic Energy Authority / Remote Applications in Challenging Environments) in Oxfordshire, the University of Bristol facility in Fenswood Farm (North Somerset), and the National Nuclear Laboratory in Cumbria, were treated to a host of robots in action, tours and a packed speaker programme.

A combination of robotic manipulators, ground, aerial and underwater vehicles along with deployment robots, plant mock-ups, and supporting infrastructure, were all showcased to demonstrate the breadth of the scheme.

Nick Sykes, Head of Operations at UKAEA’s RACE, said: “The Hot Robotics Symposium has provided a fantastic opportunity for academia and industry to learn more about research that takes place at each facility and to see some of the amazing equipment available. The valuable role that robots play in going where humans can’t, has already been proven at powerplants throughout the world and will be key in making fusion energy an environmentally responsible part of the world’s energy supply.”

These facilities support academia and industry in developing and maintaining UK skills to support work in extreme and challenging environments.

Darren Potter, Capability Leader for Plant Intervention at the National Nuclear Laboratory, said: “This was a great opportunity to showcase NNL’s facility at our Workington Laboratory, which holds industrial scale robotics, and accurate replicas of facilities on active plant. As the UK’s national laboratory for nuclear fission, this facility gives access to our unique set of capabilities, enabling ground-breaking nuclear research and development, with an offsite de-risked opportunity for academia and the supply chain to demonstrate new technology, giving users everything they need to explore brand new technologies and solutions.”

An additional benefit of Hot Robotics is to help support the development of scientists and engineers.

Chris Grovenor, Chair of the NNUF Management Group, said: “It was a pleasure to see the progress being made with the NNUF Hot Robotics programme, and I encourage the nuclear academic community to make full use of the funding available to access these world-class facilities.”

To find out more about the NNUF Hot Robotics facilities and equipment, visit: www.hotrobotics.co.uk

The question on what role communications play in forming the perception of innovative technology was discussed in the workshop “Communicating innovation: What can we do better?” on May 25th, 2022. Experts explained how the innovation uptake should be supported by effective communication of innovations: explaining the benefits, tackling risks and fears of the audiences, and taking innovation closer to the general public.

Innovation & communication experts Marta Palau Franco and Juan Antonio Pavón Losada presented these topics in depth. Also, the recipe for successful innovation communication in robotics in inspection and maintenance was presented by Carlos Matilla Codesal, CEO of FuVeX.

Marta Palau Franco, project officer at euRobotics, working on digital innovation, presented how social context and perception can affect the way we communicate innovative technologies. For example, mentioning the word “drone” in 2014 and in 2022 may not bring the same images to mind to a person. This is due to changes in context and perception around the technology over the years, going from a more military-surveillance use to others such as logistics (e.g. Amazon shipping), entertainment (e.g. drone lights, filming, drone races, etc.), inspection & maintenance, agriculture, etc.

This example emphasizes that intentional engagement with the audience requires a good understanding of the different communication contexts (i.e. social, physical, cultural) and their perception or future expectations of the technology. How can we change someone‘s perception or expectations? We can do that by changing their current references or their interpretation of their prior references.

The people’s perception of innovation might not necessarily correlate with the technological level of innovation. Marta introduced how good and bad communication can create different impressions on the technology. She presented 4 scenarios depending on the level of technological innovation and perception of being innovative (by people) (see the picture below).

The Balanced scenario is where the company’s technological innovation is high and the company is perceived as such. When there is a high level of technological innovation, but the company is not perceived as innovative, we are in the scenario of the Bad communicator. Companies that find themselves in this group should review their communications and marketing strategy and plan. The scenario of the Illusionists presents a company with low technological innovation, but with strong communication and marketing skills.The company manages to raise audience’s expectations, but risks losing the audience’s trust if the technological innovation is not delivered.

To escape the trap of a bad communicator, whose message does not come across to the audience, Marta offered some advice:

• Be aware of your communication context(s).
• Do you know what people’s perception of your company and technology is? If the perception is negative, try to change it.
• Communicate strategically at different stages of technology development. Be flexible and adapt your communication strategy/plan when necessary. If you are in silence for a prolonged period, you are losing attention. Others will be filling in this gap.
• Listen to feedback provided by your audience and value other perspectives. (e.g., surveys). Burst your bubble, and let in the external input.
• Look for professional marketing and communications advice when needed. Take it seriously.
• By communicating about your innovative technology, you are changing the social and cultural context, contributing to smoother (and safer) adoption of your technology.
• Failure is part of the learning process.

The advice on building trust with stakeholders and communicating in a crisis was suggested by Juan Antonio Pavón Losada, a Project Manager Assistant at PRISMA Lab in the Department of Electrical Engineering and Information Technology at the University of Naples Federico II. Juan is working on the internal and external design and management of European projects’ communication and dissemination.

Juan presented what symptoms define inefficient communication. Firstly, the communication misses the point if it is not consistent and relies on minimal efforts, i.e., “checking the boxes”. In addition, the lack of a defined value proposition (communicating features and activities instead of value) and strategic positioning for the different audiences drives the communication astray from its goal.

How can we solve these mistakes and create efficient communication for inspection and maintenance? Juan has elaborated on the key concepts that are especially relevant in the field of inspection & maintenance. First, the goal of communication is to build relationships, not explain things. Trust, reliability and good reputation are the key goals. In the worst-case scenario, even when an accident happens, if the relationship with stakeholders is firm it will endure the crisis. The trust will absorb the impact of the issue. Also, sufficient attention to communication includes preparation for crisis – identifying who is eligible to speak? What would be the best channel to communicate? What should be the outputs: press release? Press conference? What would be the messages and possible consequences of the messages? Such preparation would help to react quicker and mitigate the crisis.

But, to build trust you need time. That means that the communication about your innovative solution has to be consistent and start early in the process. Apart from this, Juan proposed other tips for effective communication of innovation:

• To be an engaged listener, know the domain and stakeholders, and learn the domain-specific language.
• Express yourself clearly & constantly. Communication is like any relationship – don’t expect people to be there to hear you when you have time. Make some time and show up more than once each 6 months.
• Connect your content to the events happening in the world simultaneously.
• Get communication professionals to do the work and develop high-quality material.
• Engage in new collaborations & break the bubble of your organization to receive new inputs.
• Make intentional language choices and find a balance between technical language and superficial claims without evidence (see the picture below).

Even though robotics in inspection & maintenance seems to be a rather technical and hardly engaging field for the general audience, Juan encouraged the audience to pay substantial attention to communication in their projects. The due attention converts to allocated budget, planning efforts, strategic positioning, and consistency. However, the main idea for the communication in this field revolves around the TRUST, which is built among stakeholders, whether they are tech providers, projects, institutions, developers, politicians, citizens, etc.

FuVex is a practical example of the successful communication of innovative robotics solutions in inspection & maintenance. FuVeX is a start-up whose mission is to replace manned helicopters with long-range drones in power line inspection and other aerial data capture operations. Carlos Matilla Codesal, CEO and co-founder of FuVeX, highlighted the importance of the intent behind the communication – every word must serve to reach the goal, whether it is to raise awareness, attract investment or find clients. In addition, instead of talking about technical details, which might be the most exhilarating on earth for the developers and engineers, the communication must revolve around the problem you solve. In robotics, this problem is not obviously and easily relatable to the general public (compared to, for example, problems in medicine). However, the responsibility to identify this value proposition and story is not on the audience but the one delivering the message. Vanity is not a strategy. Do not get into the trap of doing things because it feels great but is not valuable for the company. If you are communicating to an audience that is neither your customer nor your investor, but it feels great at the end of the day, you are wasting the resources on the things that matter just to your ego.

The next workshop “Policy issues in Robotics for Infrastructure & Maintenance” will be held during the European Robotics Forum in Rotterdam. If you happen to participate in the Forum, you are welcome to join the workshop on June 29th, at 9.50am (CEST). The experts will discuss how AI regulation affects the domain of Robotics for Infrastructure and Maintenance, what direction the policy is moving to and what are the issues in the pan-European legislation.

One of the essential factors for widespread robotics adoption, especially in the inspection and maintenance area, is the regulatory landscape. Regulatory and legal issues should be addressed to establish effective robotics deployment legal frameworks. Common goals of boosting the widespread adoption of robotics can only be achieved by creating networks between the robotics community and regulators.

On the 23rd of March, Maarit Sandelin, Peter Voorhans and Dr Carlos Cuevas Garcia were invited by Robotics4EU and RIMA network to discuss how cooperation among regulators and the robotics community can be fostered and what are the most pressing legal challenges for the inspection & maintenance application area of robotics.

Maarit Sandelin and Peter Voorhans from Robotic Innovation Department in SPRINT Robotics have opened the workshop with the question of why robotics are important in inspection and maintenance? Speakers highlighted three main aspects: safety, efficiency and costs. Firstly, robotic solutions allow for reducing the fatalities and risks of accidents in the environments of heights, confined spaces or under-water. Secondly, the preparation work for inspection and maintenance (shutting down the facilities, clearing and cleaning the spaces, air sampling, getting the permits) is not required for inspection and maintenance done by a robot. The bureaucracy – applying and waiting for permits – is reduced as well.

However, the integration of robots faces barriers in two main dimensions: differences in cross-border standards and acceptance of robotics by inspectors. Speaking of regulatory challenges, Peter Voorhans identified the main problems:

• The regulatory framework for acceptance in robotics is disastrous at the global level
• Robotic inspections are not always allowed based on regulations or interpretation of the regulation
• A different interpretation of regulations causes issues for service and technical providers

To move further with the integration of robots into Inspection and maintenance, the Europe-wide acceptance and legislation of robots are needed. First, the acceptance of robotics (for example, remote visual techniques) by notified bodies would be a big step further. Also, the training of inspectors should involve robotics training, so the inspectors would understand the advantages and consequences of the integration of robotics and could advocate themselves for the uptake of robotics.

Different legislation and regulations across borders mean that in each country, inspection has to be performed by local certified inspectors. For example, a Dutch company is performing an in-service inspection in France. Due to differences in legislation, a certified inspector from the Netherlands is not allowed to perform the remote visual inspection in France. A local notified body needs to be involved.

Leaving aside the national & cross-border legislation issues, Peter Voornhans has drawn attention to the company-level of policies. As an example, the internal policies in DOW, chemical and plastics manufacturer, defined that people will not be allowed in confined spaces starting from 2025. This leadership position gave a strong incentive to introduce robotics and convince inspectors to use them. The internal programme ensured the recognition and celebration of robotic use cases and best practices, ensuring higher levels of robotics acceptance overall.

Dr Carlos Cuevas Garcia, a postdoctoral researcher at the Innovation, Society and Public Policy Research Group at the Munich Center for Technology in Society (MCTS), Technical University Munich, has shared his experience in following the EU-funded projects for uptake of robotics in I&M. Dr Garcia has evaluated the policy goals and results, following the cycle of the projects, as policy instruments.

From the sociology of technology perspective, robotics in I&M plays at the unique intersection of innovation and maintenance. Innovation is done by heroic people, entrepreneurs, it is celebrated, and covered in news. Maintenance is done by invisible people, it is usually overlooked. However, such projects as RIMA, bring the two dimensions together. As innovation aims at improving maintenance, what can innovation learn from maintenance? How can maintenance improve innovation?

Speaking of the policy role in this intersection, Dr Garcia has presented the innovation policy landscape from the instrument’s perspective.

In order to improve this landscape, he identified two ways forward:

1. Examine the effects of individual policy instruments on the field of I&M robotics
2. Examine the dynamics of different instruments together, and how they enable (and constrain!) the continuity of projects

The examination could be implemented considering the vulnerabilities in the policy instruments. Drawing from the experience in observing and analysing the EU-funded projects, as instruments to achieve policy goals, Carlos identified several vulnerabilities:

• The confusion between the role of the “public end-user” and the role of the subcontractor. In the case of the observed projects, the subcontractors’ input was not formally involved, even though the maintenance is actually done by the subcontractor.
• The interest of the “public end-user” and subcontractor to purchase or keep funding the technologies of the solution after the project was not sufficient
• The “public end-user” didn’t want to directly fund a technology considered risky for workers’ jobs.
• The particular policy instrument observed (Public end user-Driven Technological Innovation) was too rigid to respond to the complexities of the situation, yet too weak to provide further directions

.

Speaking of ways to improve the policy process, Carlos identified that besides technical progress (for example, going beyond technological readiness level from 2 to 5), instruments should consider other metrics of success, e.g.:

• How well do roboticists’ teams and maintainers work together?
• How do robots empower maintainers?
• How does the team co-create a vision of the whole inspection process (service logistics, transporting, unloading, fixing robots, etc.)?

Dr Carlos concluded by suggesting a couple of policy recommendations:

• We must explore the learning trajectories of different types of stakeholders involved in sequences of I&M robotics projects;
• We have to learn how to provide maintenance to innovation networks and repair innovation policy instruments by better identifying their contradictions, fragilities and vulnerabilities;
• This requires close and durable engagement between I&M experts, roboticists, project coordinators, policymakers, regulators, and sociologists of technology.

Finally, the session was concluded with a panel discussion thematizing previous presentations, and engaging the audience. As a final conclusion, the experts suggested beginning with industry-led insights to change the paradigm of policy framework on a larger scale.

MIT researchers have developed a system that enables a robot to learn a new pick-and-place task based on only a handful of human examples. This could allow a human to reprogram a robot to grasp never-before-seen objects, presented in random poses, in about 15 minutes. Courtesy of the researchers

By Adam Zewe | MIT News Office

With e-commerce orders pouring in, a warehouse robot picks mugs off a shelf and places them into boxes for shipping. Everything is humming along, until the warehouse processes a change and the robot must now grasp taller, narrower mugs that are stored upside down.

Reprogramming that robot involves hand-labeling thousands of images that show it how to grasp these new mugs, then training the system all over again.

But a new technique developed by MIT researchers would require only a handful of human demonstrations to reprogram the robot. This machine-learning method enables a robot to pick up and place never-before-seen objects that are in random poses it has never encountered. Within 10 to 15 minutes, the robot would be ready to perform a new pick-and-place task.

The technique uses a neural network specifically designed to reconstruct the shapes of 3D objects. With just a few demonstrations, the system uses what the neural network has learned about 3D geometry to grasp new objects that are similar to those in the demos.

In simulations and using a real robotic arm, the researchers show that their system can effectively manipulate never-before-seen mugs, bowls, and bottles, arranged in random poses, using only 10 demonstrations to teach the robot.

“Our major contribution is the general ability to much more efficiently provide new skills to robots that need to operate in more unstructured environments where there could be a lot of variability. The concept of generalization by construction is a fascinating capability because this problem is typically so much harder,” says Anthony Simeonov, a graduate student in electrical engineering and computer science (EECS) and co-lead author of the paper.

Simeonov wrote the paper with co-lead author Yilun Du, an EECS graduate student; Andrea Tagliasacchi, a staff research scientist at Google Brain; Joshua B. Tenenbaum, the Paul E. Newton Career Development Professor of Cognitive Science and Computation in the Department of Brain and Cognitive Sciences and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL); Alberto Rodriguez, the Class of 1957 Associate Professor in the Department of Mechanical Engineering; and senior authors Pulkit Agrawal, a professor in CSAIL, and Vincent Sitzmann, an incoming assistant professor in EECS. The research will be presented at the International Conference on Robotics and Automation.

Grasping geometry

A robot may be trained to pick up a specific item, but if that object is lying on its side (perhaps it fell over), the robot sees this as a completely new scenario. This is one reason it is so hard for machine-learning systems to generalize to new object orientations.

To overcome this challenge, the researchers created a new type of neural network model, a Neural Descriptor Field (NDF), that learns the 3D geometry of a class of items. The model computes the geometric representation for a specific item using a 3D point cloud, which is a set of data points or coordinates in three dimensions. The data points can be obtained from a depth camera that provides information on the distance between the object and a viewpoint. While the network was trained in simulation on a large dataset of synthetic 3D shapes, it can be directly applied to objects in the real world.

The team designed the NDF with a property known as equivariance. With this property, if the model is shown an image of an upright mug, and then shown an image of the same mug on its side, it understands that the second mug is the same object, just rotated.

“This equivariance is what allows us to much more effectively handle cases where the object you observe is in some arbitrary orientation,” Simeonov says.

As the NDF learns to reconstruct shapes of similar objects, it also learns to associate related parts of those objects. For instance, it learns that the handles of mugs are similar, even if some mugs are taller or wider than others, or have smaller or longer handles.

“If you wanted to do this with another approach, you’d have to hand-label all the parts. Instead, our approach automatically discovers these parts from the shape reconstruction,” Du says.

The researchers use this trained NDF model to teach a robot a new skill with only a few physical examples. They move the hand of the robot onto the part of an object they want it to grip, like the rim of a bowl or the handle of a mug, and record the locations of the fingertips.

Because the NDF has learned so much about 3D geometry and how to reconstruct shapes, it can infer the structure of a new shape, which enables the system to transfer the demonstrations to new objects in arbitrary poses, Du explains.

Picking a winner

They tested their model in simulations and on a real robotic arm using mugs, bowls, and bottles as objects. Their method had a success rate of 85 percent on pick-and-place tasks with new objects in new orientations, while the best baseline was only able to achieve a success rate of 45 percent. Success means grasping a new object and placing it on a target location, like hanging mugs on a rack.

Many baselines use 2D image information rather than 3D geometry, which makes it more difficult for these methods to integrate equivariance. This is one reason the NDF technique performed so much better.

While the researchers were happy with its performance, their method only works for the particular object category on which it is trained. A robot taught to pick up mugs won’t be able to pick up boxes or headphones, since these objects have geometric features that are too different than what the network was trained on.

“In the future, scaling it up to many categories or completely letting go of the notion of category altogether would be ideal,” Simeonov says.

They also plan to adapt the system for nonrigid objects and, in the longer term, enable the system to perform pick-and-place tasks when the target area changes.

This work is supported, in part, by the Defense Advanced Research Projects Agency, the Singapore Defense Science and Technology Agency, and the National Science Foundation.

• PAPER – Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation Anthony Simeonov, Yilun Du, Andrea Tagliasacchi, Joshua B. Tenenbaum, Alberto Rodriguez, Pulkit Agrawal, and Vincent Sitzmann.

Machine Learning at the edge is gaining steam. BrainChip is accelerating this with their Akida architecture, which is mimicking the human brain by incorporating the 5 human senses on a machine learning-enabled chip.

Their chips will let roboticists and IoT developers run ML on device for low latency, low power, and low-cost machine learning-enabled products. This opens up a new product category where everyday devices can affordably become smart devices.

Rob Telson

Rob is an AI thought-leader and Vice President of Worldwide Sales at BrainChip, a global tech company that has developed artificial intelligence that learns like a brain, whilst prioritizing efficiency, ultra-low power consumption, and continuous learning. Rob has over 20 years of sales expertise in licensing intellectual property and selling EDA technology and attended Harvard Business School.

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By Jovita Tautkevičiūtė

Robotics4EU is a 3-years-long EU-funded project which advocates for the wider adoption of AI-based robots in 4 sectors: healthcare, inspection and maintenance of infrastructure, agri-food, and agile production. Thus, one of the ways in which Robotics4EU raises awareness about non-technological aspects in robotics is delivering a series of workshops to involve the research community, industry representatives and citizens.

The workshop “Boosting innovations and maximising societal impact. Role of Digital Innovation Hubs (DIHs) in I&M Robotics” which took place on the 23rd of February, 2022 analysed the role and contribution of Digital Innovation Hubs (DIHs) to the widespread adoption of robotics in society. How can they enhance the implementation of robotics by SMEs and startups into their daily operations? How can they help to close the knowledge gap of non-technological issues of robotics in Inspection & Maintenance (I&M)?

These questions were analysed by five experts: Ebert von Vonderen, Ladislav Vargovcik, Maria Roca, Roi Rodriguez de Bernardo and Christophe Leroux, during the presentations and panel discussions of the workshop.

Digital Innovation Hubs

What is Digital Innovation Hub (DIH) and what role does it play? Ebert van Vonderen & Ladislav Vargovcik from DIH Robotics Hub Košice explained what DIH is and how it works with various stakeholders: universities, DIH network, SMEs. In general, DIH serves as a bridge between research organizations and industry – technology and services providers, end-users and SMEs. In the case of DIH Robotics Hub Košice, technical faculties of Košice University, Prototyping and Innovation Center established the DIH.

Structure of DIH Robotics Hub Košice

A DIH serves as a one-stop-shop for SMEs to bring their questions. DIHs need to ensure that SMEs have easy access to technology innovation, are provided with relevant services and training for technology adoption. Thanks to the efforts of the RIMA network, DIHs are spread over Europe and interact for I&M possibilities, competencies, and share the available opportunities across Europe.

Why are Robotics DIHs important for SMEs? They tackle many challenges that arise for companies, aiming to boost innovation, enhance and contribute to robotics implementation. Some of the challenges mentioned by Ebert van Vonderen:

• High technology levels needed, complex tasks in I&M
• High Investments needed
• Competition, profitability, ROI (profitability is in many cases a problem)
• Ecosystem maturity, differences across Europe
• Resistances from communities
• Creation of sustainable solutions

An example of DIH Robotics Hub Košice’s cooperation with INETEC, high tech company that specialises in robotics, instruments and software was presented. Magic Lancer II – a robot for nuclear inspection, was developed under the cooperation of DIH Robotics Hub Košice.

Screenshot of the Magic Lancer II presentation

How to enhance the uptake of technology?

Christophe Leroux, representing RIMA (Robotics for Inspection & Maintainance) Network explained that RIMA Network connects and inspires key stakeholders in I&M robotics and aims to accelerate innovation and uptake of robotics between these stakeholders. Its main purpose is to help digitalise European Industry. Even though the market is estimated with a huge potential for innovation (450 bn EUR/year), the bottleneck is the adoption of robotics to market – no real connection between industry and research organisations.

RIMA Network directs its efforts to establish a network of DIHs (currently, it connects 13 DIHs), focussing on robotics in I&M, fund SEMs to support experimentation, set up courses to facilitate the uptake of technologies and develop new skills, and inform people of funding opportunities for business development.

RIMA network supports SMEs by organising open calls. 15 innovative solutions were supported, with the aim to have a social-economic impact win EU with new products, services, business, established jobs, along with the reduction of costs and risks. RIMA Network provides the following services to SMEs and research organisations:

• Advising (on finances, technology)
• Proof of Concept
• Support for tech transfer
• Support for testing
• Access to Incubator’s
• Access to value chain actors
• Training and coaching
• The market study, providing information to network partners

RIMA Network Objectives

ICT innovation for manufacturing SMEs

Maria Roca, I4MS Project Manager, presented I4MS (ICT Innovation for Manufacturing SMEs). It is the initiative promoted by the European Commission to foster the digital innovation of manufacturing SMEs in Europe in order to boost their competitiveness in the digital era. The I4MS goal is to contribute to the adaptation of European SMEs to the current digital transformation challenges by funding & mentorship, training and access to physical and virtual technology platforms. I4MS connects a community of 1700 members, 42% of them – SMEs. According to Maria Roca, the organisation has identified that geographical coverage of Europe is uneven and Eastern European countries need to be represented better in the network. Also, there is a gap in SMEs ’ understanding of technology advantages and network ability to consider SMEs’ interests.

Screenshot from Maria’s Roca presentation on I4MS and Open Call Calendar

Roi Rodriguez de Bernardo, Program Manager at FundingBox presented the AI4EU platform and funding opportunities for SMEs. AI4EU is a collaborative H2020 Project that aims to:

• mobilize the entire European AI community to make AI promises real for the European Society and Economy, and
• create a leading collaborative AI European Platform to nurture economic growth.

AI4EU aims to be a catalyst between research and industry. The platform presents available funding opportunities and various tools (research, education, ethics, services).

Speaking of the funding opportunities, Roi set the scene by capturing Europe’s position in the global AI market. The AI Business market is emerging worldwide with an estimated growth of the AI market from 40,2% CAGR from 2021 to 2028. The growing focus on AI is also evident in the EU strategy. AI strategy, set by EC offers yearly 1bn EUR funding for a period of 2021-2028, expecting to leverage 20bn EUR investments. The funding is focused on SMEs adopting AI with some cases of funding for startups or technology providers developing AI solutions that could be adopted by SMEs.

Screenshot from Roi Rodriguez de Bernardo presentation presenting Funding opportunities and open calls

Panel discussion

The discussion started with the question of whether the EU can be a powerhouse of robotics in I&M and lead the way in the race with the USA and China, also adhering to our values? It was identified that record-high investment in the industry in 2021 shows that it is possible. Even though the USA and China did not take into account the ethical aspects EU holds, they are starting to develop regulation as well. It seems that the path set by the EU is followed now by other big markets.

Considering ethical or socio-economic aspects of most importance in the field, Maria Roca identified the management of data for SMEs as a bottleneck. The difficulty lies in the hindrances to sharing data with other companies. Further, in the case of AI, ethical aspects are evaluated in all EU funded projects. From the perspective of DIH’s provided services, RIMA’s representative Christophe Leroux explained that ethical and safety aspects are taken into account in facilitated experimentation. In practice, ethical assessment is conducted for each experimentation, involving experts in the field of ethics. On a daily basis, RIMA’s work includes mentorship on ethical issues and guidelines.

The ways of enlarging the networks were also discussed. Maria Roca identified training as a main tool to attract members and showcase what they can achieve in collaboration and participation in their open calls. Networking, as a core business of DIHs, also covers the organisation of local events with experts. Also, collaboration with social scientists was emphasised as crucial in the field of AI. Annelli Rose, representative of the Robotics4EU project noted that interdisciplinary collaboration is in line with the EU priorities, where the change is foreseen to have a breeding ground.

Zooming in to the topic of robotics in I&M the social aspect of adoption was analysed. Christophe Leroux explained that there is attractiveness for robotics adoption to support certain operations because there is not much fear of losing jobs. Robots are seen as supporting their work, for example as deploying robotics in explosive, dangerous and hazardous environments. Robots are not taking away jobs, but taking away the danger. Christophe Leroux also emphasised that there is a growth in the domain of AI robotics in I&M and the main issues are related to trust and safety.

Upcoming workshop

The upcoming workshop on the 23rd of March 2022, 11-14 CET “How to make sure regulation helps and not hinders I&M robotics? Policy issues in Robotics for Inspection & Maintenance”. We will discuss how cooperation among regulators and the robotics community can be fostered and what are the most pressing legal challenges for the I&M application area of robotics. We will be investigating how to ensure the accessibility of objective information and enhance the capacity of regulators to comprehend technical aspects, risks and opportunities of robotics in I&M. We hope to leave the workshop with industry insights and specific areas for improvement for the future, which as a project we will continue to explore in our activities. Register here.

Industrial robotics

By Anastasiia Nestrogaeva (Junior Consultant at the International Projects Team, Civitta)

Robotics4EU is a 3-years-long EU-funded project which advocates for a wider adoption for AI-based robots in 4 sectors: healthcare, inspection and maintenance of infrastructure, agri-food, and agile production. Thus, Robotics4EU raises awareness about non-technological aspects in robotics through delivering a series of workshops to involve the research community, industry representatives and citizens.

The workshop “Robots: Friends and Enemies? Social impact of Robotics in Inspection and Maintenance” which took place on the 26th of January, 2022 tackled the problem of interactions between robots and humans. How to evaluate the real impact of robotics on our society? How to decide if robots are hazardous and may complicate human lives? To what extent does society accept rapidly progressing robotic technologies? All these issues were brought up during the fruitful discussion at the workshop. Around 30 people attended the workshop, with 65% of them belonging to the research community and 10% to industry representatives. About 25% comprised the general public. The participants represented 23 different countries, mostly European ones.

How to access trustworthy AI?

The workshop initiated with the presentation of the project and a brainwriting session at better involvement of participants into discussion, then four speakers shared their expertise with the participants. The first speaker was Roberto Zicari who is an affiliated professor at the Arcada University of Applied Sciences in Helsinki and an adjunct professor at the Seoul National University. Also, Roberto leads a team of international experts who defined an assessment process for Trustworthy AI, called Z-Inspection®. The speaker enlisted 4 main fundamental principles which lie down in the EU Framework: respect for human autonomy, prevention of harm, fairness, and explicability. Also, there are 7 main requirements of the EU towards the trustworthy AI: accountability; societal and environmental well-being; diversity, non-discrimination and fairness; transparency, privacy and data governance, technical robustness and safety.

Despite the existing requirements, the EU Framework does not take into account the evolving nature of the technologies and is not contextualised according to various domain peculiarities. That is why, as Roberto stressed, Z-inspection used a holistic approach and created the orchestration process which helps teams of experts access the ethical, legal, technical and domain specific implications for the use of AI products or services. This process can be employed during the whole AI lifecycle and uses socio-technical scenarios to identify issues. Then, the team of experts map to trustworthy AI (onto the ethical categories established by the EU´s Guidelines for Trustworthy AI), execute and resolve (give recommendations to stakeholders).

Embracing our future digital colleagues

Second speech was delivered by Maarit Sandelin who works as the European Network Manager in SPRINT Robotics focusing on Robotic Innovation. Maarit explained that SPRINT Robotics is an industry-driven initiative that promotes the development, availability and application of Inspection & Maintenance Robotics around the world. There are 4 focuses which include safety improvement, cost avoidance and reduction, environmental performance improvement, and general performance improvement. Maarit explained that SPRINT Robotics is an industry-driven initiative that promotes the development, availability and application of Inspection & Maintenance Robotics around the world. There are 4 focuses which include safety improvement, cost avoidance and reduction, environmental performance improvement, and general performance improvement.

Maarit stressed that COVID-19 definitely has been increasing the interest of business in robotic solutions. A total of 88% of businesses worldwide plan on adopting robotic automation into their infrastructure to increase efficiency and safety. Also, there will be a 12% increase in shipment of robots worldwide and collaborative robots (cobots) will constitute 34% of all robot sales by 2025. The speaker noticed that many dangerous inspection and maintenance activities can be done by robots and efficiency is reached mainly through the preparation stage. However, the main barriers for integrating the robotics solution into operations are regulations. In some countries, the usage of robotics is not allowed by the legislature or its interpretations which, in turn, cause issues for service and technical providers. Maarit strongly believes that the policy changes are needed to contribute to the adoption of robots.

There are also other barriers of a wide-spread adoption. Companies would need to reduce the workforce, to modify the location where a company operates, or even to modify the whole value chain. Also, the skills gaps in the local labour markets is also seen as a potential risk which would require proper training. Talking about displacement of jobs, Maarit stated that it will continue and 85 million jobs will be displaced by 2025, but 97 jobs may appear due to new division of work between humans and machines. Maarit said there is a need to show employees that robotics is not black-and-one, and reskilling may help to save a job.

BugWright2 Project

The second half of the workshop featured the BugWright2 Project which does autonomous robotic inspection and maintenance on ship hulls and storage tanks. Alberto Ortiz Rodrigez, who works as a Professor at University of the Balearic Islands, gave the information about the goals and main activities of the project. The project has the 5-steps approach: multi robot task allocation; mission planning; autonomous inspection; data post processing and actionable data generation; and maintenance which include hull cleaning, augmented reality, defect marking. Alberto showed a few examples of robots which perform different tasks. Lasly, the speaker stressed the importance of the project, enlisting 4 potential impacts. First, it demonstrates how the automated multi-robot technologies can be deployed into a large-scale industrial problem. Second, such technologies lead to cost efficiency: fuel saving, lower service costs, no immobilisation costs. Third, it brings a positive environmental impact, as it makes the ships and tanks safer, contributes to lower fuel consumption and smaller need for antifouling. Lastly, these autonomous inspections are regulated by the World Maritime Organisation (UN).

Human-Robot-Collaboration: perspectives from work and organisational psychology

The last speech was delivered by Thomas Ellwart who works as a Professor at the University of Trier. He touched the topic of the interactions between robots and humans. While bringing up the main topic of the workshop “Are robots friends or enemies?”, the speaker noticed that there is a need to define a criteria of terms “friends” and “enemies” or, in other words, criteria of functional / dysfunctional human-robot-interactions (HRC). For psychologists, the main important issue to evaluate among the HRC is a very specific task of employees on the ship. Thomas is convinced that there is a need to reflect abstract models of robotic solutions from on-site user perspective. Talking about criteria of functional HRC, they should:

• Facilitate the proper execution of the tasks
• Protect health and increase safety
• Promote individual well-being
• Develop skills and human abilities
• Avoid under/overload demands, isolated works, task hindrances, etc.

However, a high robot autonomy causes some dysfunctional effects of high robot autonomy such as exclusion of humans for safety reasons, reduced possibilities to apply and train skills, responsibilities to react in case of failures or disturbances, and low quality residual tasks. This way, humans are excluded from the task performance, but they are still in charge of system malfunctioning. This way, a high autonomy creates the high interdependence between human tasks and robots – if a robot breaks, a task is under danger, which may postpone the accomplishment of these tasks. Also, Thomas stressed that acceptance is also a double-faced matter: overtrust is as dangerous as mistrust.

To reflect all the criteria, the psychologists look at specific tasks, asking what level of autonomy the robot has and what subtasks it performs. Is it making decisions or just monitoring and implementing action? Thus, from the user perspective, there are critical factors which should be taken into account:

• Taks (low effectiveness, high costs, hindrances)
• Technology (reliability, accuracy, maintenance needs)
• Human (e.g. trust, control, cognitive load: how many robots can be overlooked by a human)
• Organisation (e.g. different stakeholders, roles)

As one the aims of Robotics4EU is to develop the Responsible Robotics Assessment Model, a main coordinator of the project, Anneli Roose, talked about social readiness of robots. Thus, in order to determine this, it is important to figure out to what extent a robot meets a) ethical values, b) socio-economic needs, c) data issues, d) education and management issues, e) legal concerns. It does not cover any legal or regulatory requirements. The foundation to build the Robotics4EU Assessment Model is the Societal Readiness Levels, while the used tools are surveys, interviews, debates, workshops, and stakeholder forums involving the robotics research community, policy makers, and citizens.

Panel discussion

Lastly, the workshop finished with the panel discussion which brought up very controversial topics.

How to design a self-assessment model or self-assessment tool in a way it would be valid in 2-3 years. Is it possible or even relevant?

Thomas Ellwart stressed that this is a quite difficult question, as it requires to involve the users and the market. Another matter is whether the automated system will be sustainable and adaptable to the changing environment and reliable over the time which is actually a question to the developers and engineers. Roberto Zicari notices that the Maturity Assessment Model should be domain specific, as a robot assisting the healthcare is very different from the robot replacing mine workers. Thus, it is quite difficult to transfer knowledge obtained in the healthcare sector to the Inspection and Maintenance. He stressed that it would be very interesting to create an interdisciplinary working group focused on several domains and try to assess societal readiness level.

Are the regulatory barriers significant? Are these barriers in the EU regulations or/and national regulations?

Thomas Ellwart states that there are 2 difficult matters: safety and data protection, which come together if we are talking about the regulation on AI. Roberto Zicari mentioned that the EU proposed the first AI Regulation Act, aimed at regulating any AI-based systems and solutions which is based on the risk assessment approach. This proposed act looks at AI mainly as a software, opposite to the classical definition. However, which exact composition of hardware and AI poses high risk and low risk? Thus, Roberto stressed that there is a need for discussions among researchers, law community and industry experts. Some participants also noticed that the regulatory framework would need to be adapted to the cultural differences all around the world.

Upcoming workshop

The upcoming workshop on the 23rd of February 2022, 11-14 CET “Boosting Innovations and Maximising Societal Impact. Role of Digital Innovation Hubs (DIHs) in Robotics for Inspection & Maintenance”. We will look at digital innovation hubs as a way to connect institutions and SMEs, helping to close the knowledge gap of non-technological issues of robotics in Inspection & Maintenance. We will focus on opportunities which bring SMEs and tech startups together and how they can potentially boost their innovations in the respected area. The workshop will feature representatives from digital innovation hubs, as well as industry experts.

In this episode, Audrow Nash speaks to Steve Macenski, who is the Open Source Robotics Engineering Lead at Samsung Research America. Steve leads the Nav2 project, which is an open source robot navigation framework. In this conversation, they talk about the problem and challenges of robot navigation, how Nav2 works at a high level, hybrid planners, and about Steve’s experience working with the Nav2 community.

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Oleg Shipitko, Chief Technical Director of Evocargo, an integrated logistics service company using autonomous vehicles speaks with Kate. Oleg talks about the need for automating operational logistics inside enclosed facilities centers and how their autonomous vehicles and other operational services can greatly improve the current way we transport goods within facilities such as ports, warehouses and factories. Oleg also discusses the sensing choices for the specific use cases of these autonomous vehicles.

Oleg Shipitko

Oleg Shipitko is the Chief Technical Director of Evocargo. He has a bachelors and masters degree in autonomous information and control systems (bachelors: 4.96/5.0, masters: 5.0/5.0), and a PhD in Space Science and Technology with focus in Mathematical modeling, numerical methods and program complexes.

Oleg has received numerous awards including: Best paper awarded at 32nd European Conference on Modeling and Simulation (ECMS-2018): Ground Vehicle Localization With Particle Filter Based On Simulated Road Marking Image and Best paper awarded at IV International Conference on Information Technology and Nanotechnology (ITNT-2018): Gaussian filtering for FPGA based image processing with High-Level Synthesis tools.

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In this episode, Audrow Nash speaks to Matt Robinson, Program Manager for ROS-Industrial Americas at the Southwest Research Institute. ROS Industrial is a group that seeks to help industrial users, for example factories, leverage ROS and its ecosystem. In this conversation, they talk about Matt’s background, the need for the ROS-Industrial group and what problems they’re working to solve, ROS-Industrial’s consortium, and ROS-Industrial’s new working group.

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The use of industrial robots in factories around the world is accelerating at a high rate: 126 robots per 10,000 employees is the new average of global robot density in the manufacturing industries – nearly double the number five years ago (2015: 66 units). This is according to the 2021 World Robot Report.

By regions, the average robot density in Asia/Australia is 134 units, in Europe 123 units and in the Americas 111 units. The top 5 most automated countries in the world are: South Korea, Singapore, Japan, Germany, and Sweden.

“Robot density is the barometer to track the degree of automation adoption in the manufacturing industry around the world,” says Milton Guerry, President of the International Federation of Robotics.

Asia

The development of robot density in China is the most dynamic worldwide: Due to the significant growth of robot installations, the density rate rose from 49 units in 2015 to 246 units in 2020. Today, China’s robot density ranks 9th globally compared to 25th just five years ago.

Asia is also the home of the country with the world´s highest robot density in the manufacturing industry: the Republic of Korea has held this position since 2010. The country’s robot density exceeds the global average seven-fold (932 units per 10,000 workers). Robot density had been increasing by 10% on average each year since 2015. With its globally recognized electronics industry and a distinct automotive industry, the Korean economy is based on the two largest areas for industrial robots.

Singapore takes second place with a rate of 605 robots per 10,000 employees in 2020. Singapore’s robot density had been growing by 27% on average each year since 2015.

Japan ranked third in the world: In 2020, 390 robots were installed per 10,000 employees in the manufacturing industry. Japan is the world´s predominant industrial robot manufacturer: The production capacity of Japanese suppliers reached 174,000 units in 2020. Today, Japan´s manufacturers deliver 45% of the global robot supply.

North America

Robot density in the United States rose from 176 units in 2015 to 255 units in 2020. The country ranks seventh in the world – ahead of Chinese Taipei (248 units) and China (246 units). The modernization of domestic production facilities has boosted robot sales in the United States. The use of industrial robots also aids to achieve decarbonization targets e.g. in the cost-efficient production of solar panels and in the continued transition towards electric vehicles. Several car manufacturers have announced investments to further equip their factories for new electric drive car models or to increase capacity for battery production. These major projects will create demand for industrial robots in the next few years.

Europe

Europe´s most automated country is Germany – ranking 4th worldwide with 371 units. The annual supply had a share of 33% of total robot sales in Europe 2020 – 38% of Europe’s operational stock is in Germany. The German robotics industry is recovering, mainly driven by strong overseas business rather than by the domestic or European market. Robot demand in Germany is expected to grow slowly, mainly supported by demand for low-cost robots in the general industries and outside traditional manufacturing.

France has a robot density of 194 units (ranking 16th in the world), which is well above the global average of 126 robots and relatively similar compared to other EU countries like Spain (203 units), Austria (205 units) or The Netherlands (209 units). EU members like Sweden (289 units), Denmark (246 units) or Italy (224 units), have a significantly higher degree of automation in the manufacturing segment.

As the only G7 country – the UK has a robot density below the world average of 126 units with 101 units, ranking 24th. Five years ago, the UK´s robot density was 71 units. The exodus of foreign labor after Brexit increased the demand for robots in 2020. This situation is expected to prevail in near future, the modernization of the UK manufacturing industry will also be boosted by massive tax incentives, the “super-deduction”: From April 2021 until March 2023, companies can claim 130% of capital allowances as a tax relief for plant and machinery investments.

Kate speaks with Brad Bogolea, CEO and Co-founder of Simbe Robotics. Simbe Robotics developed a mobile robot named Tally, which is bringing advanced shelf insights to improve the retail shopping experience.

Tally provides a state-of-the-art sensing system on a robust, scalable platform that collects analytics in real-time.

Brad Bogolea

Brad Bogolea is the CEO and Co-Founder of Simbe Robotics, where he is responsible for the company’s vision and execution of its leading retail intelligence solution. In November 2015, Brad brought to market the Tally robot, the world’s first autonomous shelf auditing and analytics solution to help retailers ensure merchandise is always stocked, in the right place, and correctly priced. The National Retail Federation Foundation has named Brad to its list of “People Shaping Retail’s Future.

Prior to Simbe, Brad spent 10 years in the energy and wireless sensor industry. Most recently Brad worked at Silver Spring Networks, where he led global product management and business development efforts for their energy management and data analytics platform for energy utilities. Products under Brad’s leadership allowed the world’s largest utility companies and their energy consumers alike to gain efficiency and visibility of their energy usage through smart sensors and data.

Brad holds a B.S. in Computer Science and Engineering from Pennsylvania State University where he also returned to serve as an Entrepreneur-in-Residence.

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Amit Goel, Director of Product Management for Autonomous Machines at NVIDIA, discusses the new collaboration between Open Robotics and NVIDIA. The collaboration will improve the way ROS and NVIDIA’s line of products such as Isaac SIM and the Jetson line of embedded boards operate together.

NVIDIA’s Isaac SIM lets developers build robust and scalable simulations. Dramatically reducing the costs of capturing real-world data and speeding up development time.

Their Jetson line of embedded boards is core to many robotics architectures, leveraging hardware-optimized chips for machine learning, computer vision, video processing, and more.

The improvements to ROS will allow robotics companies to better utilize the available computational power, while still developing on the robotics-centric platform familiar to many.

For the developers out there, check out the ISAAC ROS Github repository to implement this yourself.

Amit Goel

Amit Goel is Director of Product Management for Autonomous Machines at NVIDIA, where he leads the product development of NVIDIA Jetson, the most advanced platform for AI computing at the edge.

Amit has more than 15 years of experience in the technology industry working in both software and hardware design roles. Prior to joining NVIDIA in 2011, he worked as a senior software engineer at Synopsys, where he developed algorithms for statistical performance modeling of digital designs.

Amit holds a Bachelor of Engineering in electronics and communication from Delhi College of Engineering, a Master of Science in electrical engineering from Arizona State University, and an MBA from the University of California at Berkeley.

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In this episode, Audrow Nash speaks with Melonee Wise, former CEO of Fetch Robotics and current VP of Robotics Automation at Zebra Technologies. Melonee speaks about the origin of Fetch Robotics, her experience at Willow Garage, her experience being acquired by Zebra Technologies, challenges in the warehouse setting, on autonomous cars, and on the future of robotics.

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In this episode, Audrow Nash interviews Dave Coleman, Chief Executive Officer at PickNik Robotics. Dave speaks at a high level about what MoveIt is and what problems it helps roboticists solve, they talk about supervised autonomy, including a collaboration with NASA and MoveIt Studio, and Dave talks about MoveIt 3.0.

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In this IEEE ICRA 2021 Plenary Panel aimed at the younger generation of roboticists and automation experts, panelists Seth Hutchinson, Maria Pia Fanti, Peter B. Luh, Pieter Abbeel, Kaneko Harada, Michael Y. Wang, Kevin Lynch, Chinwe Ekenna, Animesh Garg and Frank Park, under the moderation of Ken Goldberg, discussed about how to close the gap between both disciplines, which have many topics in common. The panel was organised by the Ad Hoc Committee to Explore Synergies in Automation and Robotics (CESAR).

As the IEEE Robotics and Automation Society (IEEE RAS) explain, “robotics and automation have always been siblings. They are similar in many ways and have substantial overlap in topics and research communities, but there are also differences–many RAS members view them as disjoint and consider themselves purely in robotics or purely in automation. This committee’s goal is to reconsider these perceptions and think about ways we can bring these communities closer.”

How mobility is reshaping robotics and why this is a game-changing revolution has been researched by the International Federations of Robotics and published in the new paper “A Mobile Revolution”.

“Mobile robots have traditionally operated in industrial settings to transport parts throughout the factory or feed machines,” says Milton Guerry, President of the International Federation of Robotics. “Today, AMRs also work in applications where contact with the general public is intended. They provide information to shoppers, deliver room service orders in hotels or support police officers by patrolling city areas. IFR´s mobile revolution paper gives an overview of the main use cases for mobile robots and their most significant impacts.”

A short history of autonomous mobility

While researchers have worked on technologies for autonomous mobility since the 1940s, autonomous mobile robots have only become commercially viable over the last decade. This is primarily due to the availability of far more powerful and cheaper computing power. This has led to rapid developments in sensor, vision and analytics technologies which enable robots to connect in real-time to their environment. Today, “Autonomous Mobile Robots” show double digit growth. AMRs navigate and perform functions autonomously in industrial and service sectors and pave the way for mobile robot adoption around the world.

BlueBotics mini mobile robots in operation at ABB’s semiconductormanufacturing plant in Lenzburg, Switzerland. © ABB

Outlook

“Mobile robotics is a dynamic field of development and we expect exciting advances over the next decade,” says Milton Guerry. These advances will take place in both hardware and software. Mobile robots will become lighter and more flexible. AMRs and service robots will be able to navigate in a range of indoor and outdoor environments more easily as advances in sensors and software algorithms mean that navigation and vision become more and more precise.

Publication

• A Mobile Revolution – How mobility is reshaping robotics

RE2 Sapien 6M Robot Arm

In this episode, Shihan Lu interviews Travis Schneider, Business Development Manager at RE2 Robotics, focusing on their work on mobile outdoor manipulation. Travis introduces several robotic products and services of RE2, including RE2 Sapien robotic arms and human-centered system integration, and how they can be used to help rather than replace workers. He also shares his thoughts about challenges in human-robot collaboration.

Travis Schneider
Travis Schneider, Business Development Manager at RE2 Robotics (RE2), works to foster commercial B2B partnerships with groups interested in leveraging RE2’s robotic technology for applications in a wide variety of markets, including aviation, construction, energy, and medical. With a background in Mechanical Engineering, Travis has an intimate knowledge of many of the fundamentals associated with robotic systems including electromechanical design, motion control, and associated software.

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Matt Bilsky, founder and CEO of FLX Solutions, discusses the snake-like robot he invented called the FLX BOT. The FLX BOT consists of modular links, each with a joint that can extend and rotate to get into tight spaces. Each link includes sensors including inertial measurement units and a camera. The robot is used to navigate and work in challenging environments, such as above ceilings and within walls. Matt discusses the key innovations of his product as well as his academic and entrepreneurial journey that led him to the FLX BOT.

Matt Bilksy

Matt Bilsky, PhD, PE is the inventor of the FLX BOT, a licensed Professional Engineer, a Mechanical Engineering professor at Lehigh University, and a former repair/maintenance contractor. In 2017, he was awarded with the Lehigh University Entrepreneurship Educator of the Year. Matt has a Mechanical Engineering Ph.D. from Lehigh University focused on smart product design, Technical Entrepreneurship, and mechatronics. He holds two additional Lehigh degrees: a BS in Mechanical Engineering with an Electrical Engineering minor and a Master of Engineering degree also in Mechanical Engineering. Since he was a child Matt has been an innovator. In his basement shop he designed and built numerous electronic gadgets. In 2003 he started his first company, Mattcomp Services LLC, offering computer repair, networking, home theater, and handyman services. He also created a web hosting company in 2005, Mattcomp Hosting, including all necessary back-end components on dedicated servers.

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Do you fancy making yourself an industrial robot to enjoy at home? Jeremy Fielding, a passionate fan of mechanical engineering, did. So he built one. Good news is: he’s preparing a series of videos to teach you the whole process from scratch. How much power do you need to run 7 motors at one time? If you lose power, how do you prevent the arm from collapsing on you or dropping the load? How do you keep the cost down? He’s recorded over 100 hours of video, and he’s planning to teach you how he used the servo motors, how you can use them for your projects and how he designed his 7 axis, articulated robot.

Jeremy’s aim (website, YouTube, Twitter, Instagram) is simple: draw people to engineering with amazing projects, inspire them with ideas, then teach them how to do it. And for this video series, he’s also looking for your collaboration. So if you’ve got experience and knowledge on building this type of robots and you’d like to share it, maybe you end up being part of the series!

We’d like to thank Black in Robotics for making it possible for us to discover Jeremy. Here’s the video Jeremy has released to introduce his project:

We had to go find this guy on YouTube because he looked very cool! https://t.co/xomix1TZXo @jeremy_fielding #BlackInRobotics #NationalRoboticsWeek @RoboWeek

— Black in Robotics (@BlackInRobotics) April 9, 2021

Check out Jeremy’s YouTube channel to discover many more instructional videos. You can also support his work on Patreon.

The world´s top 10 most automated countries are: Singapore (1), South Korea (2), Japan (3), Germany (4), Sweden (5), Denmark (6), Hong Kong (7), Chinese Taipei (8), USA (9) and Belgium and Luxemburg (10). This is according to the latest World Robotics statistics, issued by the International Federation of Robotics (IFR).

“Robot density is the number of operational industrial robots relative to the number of workers,” says Milton Guerry, President of the International Federation of Robotics. “This level measurement allows comparisons of countries with different economic sizes in the dynamic automation race over time.”

The country with the highest robot density by far remains Singapore with 918 units per 10,000 employees in 2019. The electronics industry, especially semiconductors and computer peripherals, is the primary customer of industrial robots in Singapore with shares of 75% of the total operational stock.

South Korea comes second with 868 units per 10,000 employees in 2019. Korea is a market leader in LCD and memory chip manufacturing with companies such as Samsung and LG on top and also a major production site for motor vehicles and the manufacturing of batteries for electric cars.

Japan (364 robots per 10,000 employees) and Germany (346 units), rank third and fourth respectively. Japan is the world´s predominant robot manufacturing country – where even robots assemble robots: 47% of the global robot production are made in Nippon. The electrical and electronics industry has a share of 34%, the automotive industry 32%, and the metal and machinery industry 13% of the operational stock. Germany is by far the largest robot market in Europe with 38% of Europe’s industrial robots operating in factories here. Robot density in the German automotive industry is among the highest in the world. Employment in this sector rose continuously from 720,000 people in 2010 to almost 850,000 people in 2019.

Sweden remains in 5th position with a robot density of 274 units operating with a share of 35% in the metal industry and another 35% in the automotive industry.

Robot density in the United States increased to 228 robots. In 2019, the US car market was again the second largest car market in the world, following China, with the second largest production volume of cars and light vehicles. Both USA and China are considered highly competitive markets for car manufacturers worldwide.

The development of robot density in China continues dynamically: Today, China’s robot density in the manufacturing industry ranks 15th worldwide. Next to car production, China is also a major producer of electronic devices, batteries, semiconductors, and microchips.

Please find graph and press releases in other languages for download in the original article.

The environment is constantly changing and the robot has to be able to respond immediately.

Shobhit Yadav, mechatronics engineer smart industries and robotics at TNO

Automatically adapting to the situation
Dennis van der Plas, senior global lead packaging lines at Heineken, says, “We are becoming a high-tech company and attracting more and more technically trained staff. Repetitive tasks – like picking up fallen bottles from the conveyor belt will not provide them job satisfaction.” As part of the SMITZH innovation programme, Heineken and RoboHouse fieldlab, with support from the Netherlands Organisation for Applied Scientific Research (TNO), have developed a solution on the basis of flexible manufacturing: automated handling of unexpected situations.

According to Shobhit Yadav of TNO, flexible manufacturing is one of the most important developments in smart industry. “Today, manufacturing companies mainly produce small series on demand. It means that manufacturers have to be able to make many different products. This can be achieved either with a large number of production lines or with a small number that are flexible enough to adapt.” The Heineken project fell into the second category and involved developing a robot that could recognise different kinds of beer bottles that had fallen over on the conveyor. The robot had to pick them up while the belt was still moving. “The environment is constantly changing and the robot has to be able to respond immediately”, explains Shobhit. “This is a typical example of a flexible production line that automatically adapts to the situation.”

Robotics for a safe and enjoyable working environment
Industrial robots have obviously been around for a while. “The automotive industry deploys robots for welding car parts, whereas our sector uses them for automatically palletising products”, says Dennis. “But with this project we took a different approach. Our starting point was not a question of which robots exist and how they could be used. Instead, we focused on the needs and wishes of the people in the breweries, the operators who control and maintain the machines and how robots could support them in their work.”

The solution, in other words, had to lead not only to process optimisation but also ­– especially – to improved safety and greater job satisfaction. In addition, it would result in Heineken becoming a better employer. It is why Dennis and his colleague Wessel Reurslag, global lead packaging engineering & robotics, asked the operators what they would need to make their work safer and more interesting. One of the use cases that emerged was picking up bottles that had fallen over on the conveyor belt: repetitive but also unsafe as the glass bottles could break.

The lab is the place to meet for anyone involved in robotics.

Wessel Reurslag, global lead packaging engineering & robotics at Heineken

Dennis van der Plas, senior global lead packaging lines at Heineken, has a single minded focus: “Our first priority is to keep our people safe.”

Experimenting without a business case
Heineken initially made contact with RoboHouse field lab through a sponsorship project with X!Delft, an initiative that strengthens corporate innovation and closes the gap between industry and Delft University of Technology. “The lab is the place to meet for anyone involved in robotics”, says Wessel. “It is also linked to SMITZH and thus connected to TNO.”

The parties soon realised that their ambitions overlapped. Heineken was seeking independent advice and both TNO and RoboHouse were looking for an applied research project that focused on flexible manufacturing. “This kind of partnership is very valuable to all involved”, says Shobhit. “SMITZH allows us at TNO to work with current issues in the industry and establish valuable contacts, which makes our research more relevant. In turn, manufacturers have somewhere to go with their questions and problems regarding smart technologies.”

Accessible way to do research and experiment
The guys at Heineken have nothing but praise for the innovative collaboration with TNO and RoboHouse. Dennis says, “The great thing is that all project partners were in it to learn something. At RoboHouse, we had access to the expertise of robotics engineers and state-of-the-art technologies like robotic arms. We supplied the bottle conveyor ourselves and TNO also added knowledge to the mix. It is an accessible way to do research and experiment. This would be a lot more difficult with a business case, which must involve an operational advantage from the start.”

Through close cooperation, we really developed a joint product.

Bas van Mil, mechanical engineer and robot gripper expert at RoboHouse

A joint product by TNO and RoboHouse
TNO and RoboHouse distilled two research goals from the use cases presented by Heineken: enabling real-time robot control and using vision technology to direct the robots with cameras. The main challenge involved devising a solution that could be applied to Heineken’s high-speed packaging lines. TNO worked on the control and movements of the robot, while RoboHouse took on the vision technology aspect. This entailed recognising the fallen bottles, developing the system’s software-based control and building the ‘gripper’ to pick up the bottles.

In many existing robotic systems, […] the robot will do a ‘blind pick.’

Bas van Mil, mechanical engineer and robot gripper expert at RoboHouse

“Communication between the robot and the computer is very important”, explains Bas van Mil, mechanical engineer at RoboHouse. “Our input and TNO’s work were complementary. For example, RoboHouse did not possess Shobhit’s knowledge of control technology, which is indispensable for controlling the robot. Through close cooperation, we really developed a joint product.”

Every millisecond counts
The biggest challenge in detecting and tracking fallen beer bottles is that they never stop moving. As Bas explains, “They do not just move in the direction of the bottle conveyor but can also roll around on the belt itself. In many existing robotic systems, the camera takes a single photo that informs the movements of the robot. The robot will do a ‘blind pick’ with no way of knowing whether anything has changed since the photo was taken. This only works if the environment stays the same – but in this case, it doesn’t.”

The solution involves a system in which the camera and the movements of the robot are constantly connected to each other. Bas says, “Every millisecond counts as the bottle will disappear from view and the robot will still try to pick it up from the spot where it was half a second ago.” A RoboHouse programmer developed the camera software to be as fast and efficient as possible. The field lab even purchased a powerful computer running an advanced AI system especially for the project.

Shobhit Yadav, mechatronics engineer smart industries and robotics at TNO, was crucial for the controlling the robot.

We are receiving feedback as well as requests for the new robotic systems from breweries all over the globe.

Dennis van der Plas, senior global lead packaging lines at Heineken

TNO and RoboHouse then wrote a programme together that determines the speed of the robot from the moment a fallen bottle is detected. This enables the robot to move with the bottle based on its calculated speed. It is what makes this robot so different from existing ones. “The robot responds immediately to changes”, says Shobhit. “In fact, it is 30 per cent faster than the current top speed of Heineken’s bottle conveyors. As a result, it has a wide range of applications and can be used in a variety of environments with different production speeds.”

Smarter thanks to independent partners
Heineken valued not just the successful innovation but also the independent character of TNO and RoboHouse during the development process. Dennis says, “We now have a much better idea of what is technically feasible, what the challenges are and what we can realistically ask of our technical suppliers. Thanks to this project, we can act much more like a smart buyer and make smarter demands of our suppliers. This information is relevant to have, especially as we operate in such an innovative field where we do not just buy parts off the shelf. After all, if I ask too little, I will not get the best out of my project. But if I ask too much, it affects our relationship with the supplier.”

The project has also served as a source of inspiration for Dennis’s colleagues worldwide. “We have been sharing videos and reports from SMITZH on the intranet, building a kind of community within Heineken. We are receiving feedback as well as requests for the new robotic systems from breweries all over the globe.” To meet the demand, Dennis and Wessel want to supply breweries with a ready-to-use version. “We are currently looking for parties that can make the technology available and provide support services.”

Wessel Reurslag, global lead packaging engineering & robotics at Heineken, is already looking for the next step: “A robotic solution that is plug-and-play, flexible and easy to integrate in the brewery.”

Meanwhile, RoboHouse and TNO aim to continue optimising the robot. “This is only the pilot version”, says Bas. “We can still improve its flexibility, for example by installing a different vision module, thereby making the technology even more widely applicable.” Both organisations are therefore looking for use cases in which they can use the same technology to solve other problems. “We are looking at the bigger picture”, explains Shobhit. “This project could serve as a model for similar challenges in other industries.”

Platform for valuable connections
All parties emphasise the benefits of working together and sharing knowledge to achieve a successful and relevant innovation. “There are very few places like SMITZH where manufacturing companies can go with these kinds of questions”, says Shobhit. “SMITZH is quite unique in that sense and is vitally important because it offers a specific platform for appropriate collaborations.” Wessel agrees: “If there is one thing this project has made clear, it is that producers like Heineken, tech companies and knowledge institutions should collaborate more intensively to substantiate these kinds of projects. You won’t find a solution in a PDF or a presentation. There can be no genuine solution until it is made real and put into practice.”

Do you have a similar use case and are you, like Heineken, searching for a practical solution? Or would you first like to learn more about RoboHouse or this specific project? Feel free to contact our lead engineer Bas van Mil, or email SMITZH with questions about its collaboration programme, or contact Shobhit Yadav at TNO more information.

SMITZH and future of work fieldlab RoboHouse
SMITZH is an innovation programme focused on smart manufacturing solutions in West Holland. It brings together supply and demand to stimulate the industrial application of smart manufacturing technologies and help regional companies innovate. Each SMITZH project consists of at least one manufacturing company and a fieldlab. RoboHouse served as the fieldlab in this project.

The post Rapid Robotics for Operator Safety: What a Bottle Picker Can Do appeared first on RoboValley.

In this episode, Abate interviews Erin Bishop from Sense Photonics about the technology in their “Solid State” LiDAR sensors that allows them to detect objects more accurately and over a larger field of view than traditional scanning LiDAR. Erin dives into the technical details of Solid State Lidar, discusses the applications and industries of the technology.

Erin Bishop

Erin operates at the intersection of product management, project engineering, customer development, and product-market fit within 3D camera company Sense Photonics. Over the past several years, she has worked to initiate the market appetite for telepresence robots, indoor mobile robots, and robotic picking in warehouses. Erin has made appearances at CES, UX Week, RoboBusiness, HardwareCon, and Mobile Future Forward.

Erin creates partnerships that result in large-scale deployment of the company’s revolutionary solid-state FLASH architecture. Prior to joining Sense, she held product manager, marketing, and research roles with several robotics companies, including Industrial Perception, Inc. – which was acquired by Google in 2013 – Adept Technology, iRobot, and DEKA. Erin earned her Master’s in Mechanical Engineering from UMass Lowell, where she performed extensive research in Human-Robot Interaction.

Links

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In this episode, we hear from Luca Colasanto, Senior Robotic Scientist at Realtime Robotics, about real-time robot motion planning in dynamic and complex environments with human-robot collaboration. Realtime Robotics focuses on accelerating conventional motion planning through optimization of algorithms and hardware to allow safe use of robotic tools in work areas with humans. Luca spoke to our interviewer Kate about Realtime Robotic’s fast motion planning technology, including key aspects, such as perception, algorithms and custom hardware.

Luca Colasanto

Luca Colasanto is a Sr. Scientist at Realtime Robotics focusing on AI-based grasping and multi-robot optimization. Luca completed his PhD in Humanoid Robotics at Italian Institute of Technology, focusing on control systems for bipedal walking machines and compliant actuators. During the past ten years, Luca has worked with successful startups to design and prototype new robotics products, and with science and technology leaders, such as Robert Bosch LLC (CA), Institute for Human and Machine Cognition (FL), and École Polytechnique Fédérale de Lausanne (Switzerland), He is the listed inventor in two patent applications and the author of several scientific publications. Luca speaks four languages and is a keen traveler, having visited or lived in more than 20 countries.

Links

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In this episode, Audrow Nash speaks with Dave Coleman, CEO of PickNik Robotics, about the open source robotics manipulation platform called MoveIt. Coleman talks about MoveIt’s story, from inception and the early days to development and maintenance, as well as how MoveIt relates to the Robot Operating System (ROS) and their move to support ROS-2. He also speaks about MoveIt’s implementation, including global versus local planners and what that means. Coleman concludes by talking about World MoveIt Day and how those interested can begin learning MoveIt and contributing.

Here is a 2017 montage of how MoveIt has been used:

Dave Coleman

Dave Coleman completed his PhD in Computer Science at CU Boulder focusing on motion planning and his B.S. at Geogia Tech in Mechanical Engineering. Coleman has 12 years experience working in the field of robotics automation and is a leader in the open source MoveIt and ROS communities. His insights into robot-agnostic platforms for different morphologies, theoretical approaches, and different end-user requirements give him a well-rounded understanding for powerful robotic software. He has worked and consulted for all types of robotics companies including Google Robotics, Open Robotics, and Willow Garage.

Links

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In this episode, Lilly interviews Kajal Gada on her work at BrainCorp, the San Diego-based company behind BrainOS, a technology stack for autonomous solutions. Kajal discusses BrainCorp’s cloud-connected operating system and their floor cleaning, vacuuming, and warehouse delivery robots. She also articulates some of the challenges in becoming a software engineer and developing commercial solutions.

Kajal Gada

Kajal Gada is a software engineer at BrainCorp. She works on developing algorithms for realizing robotic operations in the real world. Kajal received her Masters in Robotics from University of Maryland – College Park, and also has an MBA in Technology Management from NMIMS, Mumbai. She is an advocate for women in the workplace and has spoken at GHC in 2018. In her free time, she loves to read mystery novels and work on puzzles

Links

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In this episode, Abate interviews David Mindell, co-founder of Humatics. David discusses a system they developed that can detect the location of a special tracking device down to a centimeter level accuracy. They are currently developing a device to detect location down to a millimeter level accuracy. This solves a the core problem of localization for robots. David discusses the technology behind these products and their applications.

David Mindell

David co-founded Humatics with a mission to revolutionize how people and machines locate, navigate and collaborate. He is a professor of Aeronautics and Astronautics at MIT, as well as the Dibner Professor of the History of Engineering and Manufacturing, and Chair of the MIT Task Force on the Work of the Future. He has participated in more than 25 oceanographic expeditions and is an inventor on 8 patents in autonomous helicopters, AI-assisted piloting, and RF navigation. He is the author of five books, including Our Robots, Ourselves:Robotics and the Myths of Autonomy (2015) and Digital Apollo: Human and Machine in Spaceflight (2008). David has undergraduate degrees from Yale and a Ph.D. from MIT.

Links

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In this episode, Lilly interviews Juxi Leitner, a Postdoctoral Research Fellow at the Queensland University of Technology and Co-Founder/CEO of LYRO Robotics. LYRO spun out of the 2017 win of the Amazon Robotics Challenge by Team ACRV. Here Juxi discusses deep learning, computer vision, intent in grasping and manipulation, and bridging the gap between abstract and low-level understandings of the world. He also discusses why robotics is really an integration field, the Amazon and other robotics challenges, and what’s important to consider when spinning an idea into a company.

Juxi Leitner

Juxi Leitner is co-founder of LYRO Robotics, a deep-tech startup based in Brisbane, Australia, creating robotic picking and packing solutions. LYRO is a spin-out of the Australian Centre of Excellence for Robotic Vision (ACRV), where Juxi is the research lead for the manipulation research stream (previously Vision and Action project). His research focus is on integrating Robotics, Computer Vision and Machine Learning/Artificial Intelligence (AI) for robust grasping and manipulation in real-world scenarios. In 2017 his team won the Amazon Robotics Challenge. Juxi is active in the local Brisbane deep-tech ecosystem and started Brisbane.AI and the Brisbane robotics interest group.

Links

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“The set-updemonstrates various techniques that provide a cheaper solution, increased robustness and increased safety for operators”, says Søren Peter Johansen Technology Manager at DTI.

“For example, there is a force-torque sensor in the robot which is used to manoeuvre things carefully into place, and also to increase the confidence of the operators. The force-torque sensor will sense and prevent collisions with people before the built-in safety features of the robot stop the robot arm. Thus, we can also increase safety and confidence by working with collaborative robots”, he says.

Increased effectiveness
Danfoss in Gråsten has tested the robot in connection with the company’s production of frequency converters.

“A frequency converter contains, amongst other things, capacitors assembled in groups of two or four. We have automated this process in that a robot picks up a capacitor and puts it into a fixture. For the capacitor to be correctly placed in the fixture, the electrodes must point in the right direction. Therefore, a camera sees how the electrodes are oriented in the gripper, and orients correctly before it is placed in the fixture”, explains Søren Peter Johansen.

“Then we pick a nut that has to be put on and the robot picks up the socket handle and screws the nut tight. And meanwhile the operator can do something else and thus increases efficiency”, he elaborates.

Three reasons to automate
Peter Lund Andersen, Senior Manufacturing Specialist at Danfoss, says that Danfoss is automating for several reasons:

“It is primarily about work and environmental considerations – better ergonomics and less heavy lifting. We believe that with robots, that can do the work uniformly each time, we can increase the quality of our products while maintaining safety and reducing costs”, he says, adding “The cooperation with DTI gives us the opportunity to come out and try some new things. Some things that aren’t quite mature yet and can’t just be taken off a shelf. That way we are at the forefront with everything.”

If you want to watch more videos, you can explore ROBOTT-NET’s pilot projects on our YouTube-channel.

In this episode, Lauren Klein speaks with Dr. Rand Voorhies, co-founder and CTO of inVia Robotics. In a world where consumers expect fast home delivery of a variety of goods, inVia’s mission is to help warehouse workers package diverse sets of products quickly using a system of autonomous mobile robots. Voorhies describes how inVia’s robots operate to pick and deliver boxes or totes of products to and from people workers in a warehouse environment eliminating the need for people to walk throughout the warehouse, and how the actions of the robots are optimized.

Rand Voorhies

Rand Voorhies is the CTO and a founder of inVia Robotics, where he leads the engineering team in developing the software which drives their system of autonomous robots. Voorhies received his PhD in computer science from the University of Southern California, where he conducted robotics research with NASA’s Jet Propulsion Laboratory.

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In this episode, Ron Vanderkley interviews Mark Pivac, Chief Technical Officer and co-founder of FBR (formerly Fastbrick Robotics) about the world’s first end-to-end autonomous bricklaying robot, ‘Hadrian X’. Three years after his first interview, we catch up with Pivac to see how FBR has expanded its operation and chat about their latest commercial prototype, ‘Hadrian X’, as well as the future of the robotic construction industry.

Mark Pivac is the primary inventor of FBR’s automated bricklaying technology. He is an aeronautical and mechanical engineer with over 25 years’ experience working on the development of high technology equipment ranging from lightweight aircraft to heavy off-road equipment. He has 20 years’ experience working with pro/engineer 3D CAD software as well as high-level mathematics, including matrix mathematics, robot transformations and vector mathematics for machine motion. Mark has also worked extensively with design, commissioning and fault finding on servo controlled motion systems achieving very high dynamic performance.

Links

• FBR Website
• Marc Pivac Profile Page
• Hadrian X in action (Video)
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In this episode, join our interviewer Andrew Vaziri at Promat 2019, the largest expo for manufacturing and supply chain professionals in North and South America. Andrew interviews a handful of companies which provide warehouse fulfillment robots that can autonomous pick and place items. Our guests explain how advances in AI have made autonomous picking possible. They also talk about the unique technologies they use to stand out in a crowded field of competing products.

The guests in the order they appear are:
Vince Martinelli, Head of Product and Marketing, RightHand Robotics
Jim Liefer, CEO, Kindred
Pete Blair, VP of Marketing, Berkshire Grey
Sean Davis, Technical Product Manager, Osaro
Erik Nieves, CEO, Plus One Robotics

See video of each robot picking items on our YouTube page:

Links

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• Kindred Website
• Berkshire Grey’s Website
• Osaro’s Website
• PlusOne Robotics’ Website
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In this episode, Audrow Nash interviews Alexandros Kogkas, Katie Driggs-Campbell, and Martin Karlsson about the work they presented at the 2018 International Conference on Intelligent Robots and Systems (IROS) in Madrid, Spain.

Alexandros Kogkas is a PhD Candidate at the Imperial College London and he speaks about an eye tracking framework to understand where a person is looking.  This framework can be used to understand a person’s intentions, for example to hand a surgeon the correct tool or helping a person who is paraplegic.   Kogkas discusses how the framework works, possible applications, and his future plans for this framework.

Katie Driggs-Campbell is a Post Doctoral Researcher at Stanford’s Intelligent System Laboratory and is—soon to be—an Assistant Professor at University of Illinois Urbana-Champaign (UIUC). She speaks about making inferences about the world from human actions, specifically in the context of autonomous cars.  In the work she discusses, they use a model of a human driver that they use infer what is happening in the world, for example a human using a crosswalk. Driggs-Campbell talks about how they evaluate this work.

Martin Karlsson is a PhD student at Lund University in Sweden, and he speaks about a haptic interface to mirror robotic arms that requires no force sensing.  He discusses a feedback law that allows a mirroring of forces and his future work to deal with joint friction.

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In order to consolidate Europe’s expertise in advanced manufacturing, the H2020 project ROSIN supports EU’s strong role within ROS-Industrial. It will achieve this goal through three main actions on ROS: ensuring industrial-grade software quality; promoting new business-relevant applications through so-called Focused Technical Projects (FTPs); supporting educational activities for students and industry professionals on the one side conducting ROS-I trainings as well as and MOOCs and on the other hand by supporting education at third parties via Education Projects (EPs).

Now it is easier to get an overview of first results from ROSIN at http://rosin-project.eu/results.

Focused Techincal Projects (FTPs) supported by ROSIN

Browse through vendor-developed ROS drivers for industrial hardware, generic ROS frameworks for industrial applications and model based tooling. Thanks to ROSIN support, all of these new ROS components are open-sourced for the benefit of the ROS-Industrial community. Each entry leads to a minipage that is maintained by the FTP champion, so check back often for updates on the progress of the projects.

The project is continuously accepting FTP project proposals to advance open-source robot software. New incoming proposals are evaluated every 3 months (approximately). The next cut-off dates will be 14. September and 16. November 2018. Further calls can be expected throughout the project runtime (January 2017 – December 2020).

In this interview, Audrow Nash interviews Amruta Moktali, VP of Product Management at Salesforce Analytics, about Salesforce Analytics’ analytic and artificial intelligence software. Moktali discusses the data-pipeline, how data is processed (e.g., noise), and how insights are identified.  She also talks about how dimensions in the data can be controlled for (such as race, gender, or zip-code) to avoid bias and how other dimensions can be selected as actionable so Salesforce can make recommendations—and how they use interpretable methods so that these recommendations can be explained.  Moktali also tells about her professional path, including going from computer engineering and computer science to product management and her experience with intrapreneurship (that is, starting an endeavor within a large organization).

Here is a video demo of Einstein Analytics, and you can watch Moktali’s live in the Einstein Analytics keynote at Dreamforce on Thursday, Sept. 27 at 5pm PT at salesforce.com/live and youtube.com/user/dreamforce.

Amruta Moktali
Amruta Moktali, VP of Product Management for Salesforce Analytics, has spent 10+ years immersed in the data and analytics side of popular products. Before Salesforce, she was head of product at Topsy Labs, the social search and analytics company, where her team pinpointed the catalyst tweets that initiated the Arab Spring in Egypt. Topsy was acquired by Apple and is now part of Apple Search technology. Prior to that she worked at Microsoft where she worked on several products including Bing, which she had a hand in shaping with the Powerset team. She earned her bachelor’s degree in computer engineering at Maharaja Sayajirao University in India, and her master’s in computer science at Arizona State University.

Links

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It’s been two years since the European project ROBOTT-NET kicked off and more than 60 projects have been executed with success and 1500 people have come to visit ROBOTT-NETs Open Labs. ROBOTT-NET has among other things helped nurses in F&P Robotics’ voucher. The project “Robot Assistant for Nurses” (RAN) aimed to automate frequent and labor intensive tasks of material preparation before the taking of blood samples.

ROBOTT-NET also helped make home builders’ lives easier with Robot at Works voucher project. The goal of the project was to use digital data in building projects to automate production directly on the building site.

The robot network has also worked with the Swastebot – a specialised waste sorting robot. The company Sadako developed a disruptive system for the recovery of valuable materials such as cans, bottles, bricks etc. in waste treatment plants. The system is based on artificial intelligence for material detection.

In total ROBOTT-NET has worked with 62 projects with companies such as Danfoss, Kärcher, Air Liquide and Nissan.

Now, eight projects have been selected for a Pilot to strengthen robotics development and the competitiveness of European Manufacturing even further. The projects will be teamed up with a real-world use cases and ROBOTT-NET will take the projects all the way to the market – to make even more robotics ideas in industrial and professional service robotics a reality.

You can check out what ROBOTT-NET has achieved so far in this video:

If you want to stay updated on ROBOTT-NET and the events in the projects, you can follow ROBOTT-NET on Twitter and LinkedIn.

In this episode, Audrow Nash interviews Chris McCool and Chris Lehnert about different projects that relate to agriculture at the Queensland University of Technology. McCool speaks about a large robot for weed management in fields. The robot uses Real-time kinematic GPS (very accurate) and a camera with deep learning to recognize various types of plants. Lehnert speaks about a robot to harvest sweet peppers. The robot first grabs on to the sweet pepper with a suction cup and then uses a small saw to cut the fruit from the bush. Chris speaks about using their method for other crops, how their robot does in terms of deployment, and the future of agriculture.

A video of the AgBot II, the weeding robot that McCool discusses.

https://www.youtube.com/watch?time_continue=173&v=15tovWSnJe0

A video of Harvey, the robot to harvest sweet peppers that Lehnert discusses.

Chris McCool

Chris McCool is a Senior Researcher the Queensland University of Technology (QUT).  McCool is interested in applied image classification and its ability to help robots interact with their environment.  He holds a PhD and Batchelor’s from QUT.

Chris Lehnert

Chris Lehnert is a Robotics Lecturer at the Queensland University of Technology (QUT). He is interested in robotic manipulation, specifically in agriculture and for warehouse automation. Lehnert holds a PhD from QUT and a Bachelor of Engineering from the University of Queensland.

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The pilot projects

The three companies’ accomplished voucher work varies a lot and sets the basis to enlarge the array of industrial challenges tackled in the pilot work of ROBOTT-NET.

Linak’s pilot project will address some of the barriers that are still hindering the introduction of robots in High-Mix-Low-Volume (HMLV) production. This pilot will focus on the development of a modular assembly robot solution called MARS, that can be introduced in a wide-range of industries. You can check out Linak’s voucher project here:

Irabia’s pilot project UrbanPestPatrol aims to use robots for pest management in industrial facilities. The purpose of the project is to minimize the risk of a pest infestation and its resulting cost. You can learn about Irabia’s voucher project here:

https://www.youtube.com/watch?v=1zmHDOa1ErU

Lastly, Nissan will be looking into automated kitting for complex and diverse applications. This pilot will build a robotic test cell that enables Nissan Motor Manufacturting UK to develop automated kitting capability for its part families. Find out more about Nissan’s voucher project here:

ROBOTT-NET pilots help companies develop their voucher work through proof-of-concept level and accelerate it towards commercialisation. A pilot project will be a medium-scale research prototype installation, lasting for up to 18 months developing the robot technology and business case explored in the voucher stage and applying it to an industrial application demonstration at an end user’s site.

If you want to learn more about the pilot projects, you can check out ROBOTT-NETs website.

At its production facility in St. Leon-Rot in Baden-Württemberg in South Germany, Piccolo K+L Behältertechnik GmbH manufactures skeleton and transport containers for the automotive industry. Because the company produces relatively small volumes with high variant diversity, the assembly workers at the factory perform all welding tasks by hand.

Under such conditions, the use of robot systems is simply not cost-effective. Reason: The manufacturer-specific programming languages are so complex that experts must be called in to program a robot for new components. In addition, under present circumstances it would take 90 minutes to program a new welding task, even for a simple component. This is acceptable for mass production but not for lot sizes of 20 to 100 pieces, which is often the case at Piccolo K+L Behältertechnik GmbH.

The lack of skilled workers is also a problem when high-quality welded parts are manufactured: This requires sound knowledge and longstanding experience. But suitably skilled workers are becoming increasingly scarce. It therefore makes sense to increasingly automate production processes and transfer existing expert knowledge to robot systems. For SMEs to use welding robots profitably, they need to be adapted to flexible, customer-specific production methods.

To achieve this, researchers working in the Department of Robot and Assistive Systems at Fraunhofer IPA have developed a manufacturer-independent software RobotKit specifically for welding tasks.

The software identifies possible weld seams on components in the CAD model and automatically generates a complete robot program with all movement commands.

Via a graphic interface, welders can simulate the entire process and correct it as required. This enables end-users to start using a robot for new welding tasks without delay and without the need for in-depth programming knowledge.

If product variants change, the respective welding tasks are easy and fast to reconfigure. To move parts freely on the welding table and thus reduce the necessary devices, the position of components can be accurately measured by the integrated 3-D sensor system and the robot program adjusted automatically.

Any geometric discrepancies on components can also be identified, such as those resulting from inaccuracies arising in the course of prior manual welding processes. The software then automatically adjusts the robot trajectory and processes parameters, doing away with the need for manual re-teaching in off-line programming mode.

Thanks to the intuitive software with the innovative algorithms for calculating trajectories and identifying components in 3-D, in an initial test scenario robot, programming times at Piccolo K+L Behältertechnik GmbH could be cut from 90 to just 7 minutes.

Based on these promising results, the technology is now being developed for serial application as one out of eight pilot projects in ROBOTT-NET.

In this interview, Audrow Nash speaks with Sangin Park, Senior Research Engineer at Hyundai, about exoskeletons. Park describes three exoskeleton prototypes: one for helping workers reduce back pain, one for assisting a person with paraplegia, and an exoskeleton for soldiers. Park discusses the sensors and actuators of each exoskeleton, as well as Hyundai’s exoskeleton ambitions.

Sangin Park

Sangin Park is a Senior Research Engineer at Hyundai.

Links

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• Download MP3 (12.6 MB)

Toyota HSR Trained with DART to Make a Bed.

In this episode, Audrow Nash speaks with Michael Laskey, PhD student at UC Berkeley, about a method for robust imitation learning, called DART. Laskey discusses how DART relates to previous imitation learning methods, how this approach has been used for folding bed sheets, and on the importance of robotics leveraging theory in other disciplines.

To learn more, see this post on Robohub from the Berkeley Artificial Intelligence Research (BAIR) Lab.

Michael Laskey

Michael Laskey is a Ph.D. Candidate in EECS at UC Berkeley, advised by Prof. Ken Goldberg in the AUTOLAB (Automation Sciences). Michael’s Ph.D. develops new algorithms for Deep Learning of robust robot control policies and examines how to reliably apply recent deep learning advances for scalable robotics learning in challenging unstructured environments. Michael received a B.S. in Electrical Engineering from the University of Michigan, Ann Arbor. His work has been nominated for multiple best paper awards at IEEE, ICRA, and CASE and has been featured in news outlets such as MIT Tech Review and Fast Company.

Links

• AUTOLAB
• Michael Laskey’s website
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• Download MP3 (13.2 MB)

Trumpf, Maser, Picolo, Weibel and Air Liquide are the five companies that will have their technology implemented in a pilot on a real-world use case.

Their voucher work varies greatly. Whilst Trumpf wanted to find out if automated handling of a large variety of sheet metal parts was possible, Picolo was working on generating welding robot programs. Weibel concentrated on flexible PCB Soldering and Air Liquide focused on autonomous goods picking, handling and transportation in industrial environments. Finally, Maser investigated automated systems to detect types of defects in chromed parts.

ROBOTT-NETs mission is to collect and share the latest knowledge about robot technology that can improve production, bring new ideas to market and ensure economic competitiveness.

The pilot will help the companies develop their voucher work through proof of concept level and accelerate it towards commercialisation. It will be a medium-scale research installation that will last for up to 18 months, developing the robot-technology and business case explored in the voucher stage, and applying it to an industrial demonstrator at an end user’s site.

When selecting the five projects, ROBOTT-NET was looking for pilots that will scale well across new applications and create high impact on markets through enhanced productivity, competition and disruption. Scalability and market impact were key measures in the Pilot application.

Other than these five projects, there are more pilots to look forward to as three more pilots will be announced soon.

If you want to know more about the five projects that have been selected for a pilot you can find them on ROBOTT-NET or ROBOTT-NET’s YouTube channel.

Trumpf

Maser

Picolo

Weibel

Air Liquide
https://www.youtube.com/watch?v=3ee-Om-fX4I

Note: ROBOTT-NET will be at HANNOVER MESSE from April 24-27, 2018. If you are there, make sure you pass by Stand G46 in Hall 6 by the European Commission and see project results from EU-funded projects like nextgenio, ultraSURFACE, covr, fed4sae, DiFiCIL, IPP4CPPS, Smart Anything Everywhere (SAE), RADICLE, cloudSME, BEinCPPS, CloudiFacturing & Fortissimo.

In this interview, Audrow speaks with Andrea Bajcsy and Dylan P. Losey about a method that allows robots to infer a human’s objective through physical interaction. They discuss their approach, the challenges of learning complex tasks, and their experience collaborating between different universities.

Some examples of people working with the more typical impedance control (left) and Bajcsy and Losey’s learning method (right).

To learn more, see this post on Robohub from the Berkeley Artificial Intelligence Research (BAIR) Lab.

Andrea Bajcsy
Andrea Bajcsy is a Ph.D. student in Electrical Engineering and Computer Sciences at the University of California Berkeley. She received her B.S. degree in Computer Science at the University of Maryland and was awarded the NSF Graduate Research Fellowship in 2016. At Berkeley, she works in the Interactive Autonomy and Collaborative Technologies Laboratory researching physical human-robot interaction.

Dylan P. Losey

Dylan P. Losey received the B.S. degree in mechanical engineering from Vanderbilt University, Nashville, TN, USA, in 2014, and the M.S. degree in mechanical engineering from Rice University, Houston, TX, USA, in 2016.

He is currently working towards the Ph.D. degree in mechanical engineering at Rice University, where he has been a member of the Mechatronics and Haptic Interfaces Laboratory since 2014.  In addition, between May and August 2017, he was a visiting scholar in the Interactive Autonomy and Collaborative Technologies Laboratory at the University of California, Berkeley.  He researches physical human-robot interaction; in particular, how robots can learn from and adapt to human corrections.

Mr. Losey received an NSF Graduate Research Fellowship in 2014, and the 2016 IEEE/ASME Transactions on Mechatronics Best Paper Award as a first author.

Links

• InterACT Lab
• MAHL Lab
• Andrea Bajcsy’s website
• Dylan’s Losey’s website
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• Support us on Patreon
• Download MP3 (14.9 MB)

In this episode Abate talks with Zhe Zhang from Perceptin where they are building embedded platforms for robots to do Simultaneous Localization and Mapping (SLAM) algorithms in real time. Zhe explains the methods they incorporate such as sensor fusion and hardware synchronization to make a highly accurate SLAM platform for IOT, consumer, and automotive grade robots.

Zhe Zhang

Zhe is the co-founder and CEO of PerceptIn. Prior to founding PerceptIn, he worked at Magic Leap in Silicon Valley and prior to that he worked for five years at Microsoft. Zhang has a PhD in Robotics from the State University of New York and an undergraduate degree from Tsinghua University.

Further research recommended by Zhe:

1. Probabilistic Data Association for Semantic SLAM
2. A Comparative Analysis of Tightly-coupled Monocular, Binocular, and Stereo VINS
3. Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art

Links

• Perceptin Website
• Zhe’s personal website
• Download mp3 (17.0 MB)
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• Support us on Patreon

Image: ICRA 2017

In this episode, Audrow Nash interviews several companies at the International Conference for Robotics and Automation (ICRA). ICRA is the IEEE Robotics and Automation Society’s biggest conference and one of the leading international forums for robotics researchers to present their work.

Interviews:

Howard Michel, Chief Technology Officer of UBTECH Education, on a humanoid robot for STEM (Science, Technology, Engineering, and Math) education for a range of ages. (Begins at 1m 20s.)

Li Bingbing, Software Engineer and Cofounder of Transforma in Singapore, on a robot for painting tall buildings. (Begins at 10m 25s.)

Xianbao Chen, Associate Researcher at Shanghai Jiao Tong University, on a hexapod robot for large parts machining. (Begins at 17m 5s.)

Lester Teh Chee Onn, Environmental Engineer at Advisian, on a watercraft for environmental monitoring. (Begins at 27m.)

Links

• Download mp3 (18.8 MB)
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In this interview, Audrow Nash interviews Marco Hutter, Assistant Professor for Robotic Systems at ETH Zürich, about a quadrupedal robot designed for autonomous operation in challenging environments, called ANYmal. Hutter discusses ANYmal’s design, the ARGOS oil and gas rig inspection challenge, and the advantages and complexities of quadrupedal locomotion. 

Here is a video showing some of the highlights of ANYmal at the ARGOS Challenge.

Here is a video that shows some of the motions ANYmal is capable of.

Marco Hutter

Marco Hutter is assistant professor for Robotic Systems at ETH Zürich since 2015 and Branco Weiss Fellow since 2014. Before this, he was deputy director and group leader in the field of legged robotics at the Autonomous Systems Lab at ETH Zürich. After studying mechanical engineering, he conducted his doctoral degree in robotics at ETH with focus on design, actuation, and control of dynamic legged robotic systems. Beside his commitment within the National Centre of Competence in Research (NCCR) Digital Fabrication since October 2015 Hutter is part of the NCCR robotics and coordinator of several research projects, industrial collaborations, and international competitions (e.g. ARGOS challenge) that target the application of high-mobile autonomous vehicles in challenging environments such as for search and rescue, industrial inspection, or construction operation. His research interests lie in the development of novel machines and actuation concepts together with the underlying control, planning, and optimization algorithms for locomotion and manipulation.

Links

• Download mp3 (11.1 MB)
• Robotic Systems Lab
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Over 400 people attended the all-day series of panels with well-known speakers and relevant subject matter. Panels covered mobility, agricultural robotics, search and rescue, and the retail and manufacturing revolutions. Henrick Christensen from UC San Diego gave an overview of robotics in Japan and the US as a keynote. He described the key drivers propelling the robotics industry forward and the digitization of manufacturing: mass customization, unmanned vehicles, the aging society (particularly in Japan), and the continuing need for application-specific integrators.

He was followed by Atsushi Yasuda from METI, Japan’s Ministry of Economy, Trade and Industry (the agency that funds NEDO) who emphasized Japan’s need to focus on technologies that can safely assist their aging population. Manufacturing, agriculture, nursing and medical care, plus disaster relief were points he detailed.

I was the moderator of a panel on The Manufacturing Revolution: Automated Factories with speakers from Yaskawa (Chetan Kapoor), Yamaha (Hiro Sauou), OMRON/Adept (Edwardo De Robbio), GE (Steve Taub) and VEO Robotics (Patrick Sobalvarro). Trends in this arena are being driven by the global movement toward mass customization and the need for flexibility in automation and robotics. For the next while that flexibility will use humans in the loop to collaborate with their robot counterparts.

There was also an exhibition with around 25 companies and agencies participating in a pop-up type of trade show. It was noisy, fun and informative.

Best line from the investment panel: “Invest in missionaries; not mercenaries.” 

Second best line came from Henrik Christensen regarding measuring the successfulness of home robots by their “time to boredom.”

Most interesting question and answer about the future came from James Kuffner, the CTO of Toyota Research Institute who said that Toyota asked the Institute what the company should to do after self-driving reduces the size of the car industry. Kuffner said that Toyota decided to “pivot to robotics and particularly to assistance robots for health, elder and home care.”

In the panel on unmanned vehicles, the consensus was that mapping, proprietary driving data, regulation and weather were all holdups thwarting fully autonomous vehicles (Level 5 vehicles (without pedals or a steering wheel)). Because of those problems, it was their opinion that only Level 4 would be achieved in the next decade.

NEDO’s 2017 fundings total $1.17 billion and include $99.1 million for robot technology seed and mid-term fundings for practical robotic solutions. Current projects include infrastructure inspection and maintenance, disaster response robots, elder care robots, and next-generation technologies in industrial and service robots and AI.

In this episode, Audrow Nash interviews Chris Gerdes, Professor of Mechanical Engineering at Stanford University, about designing high-performance autonomous vehicles. The idea is to make vehicles safer, as Gerdes says, he wants to “develop vehicles that could avoid any accident that can be avoided within the laws of physics.”

In this interview, Gerdes discusses developing a model for high-performance control of a vehicle; their autonomous race car, an Audi TTS named ‘Shelley,’ and how its autonomous performance compares to ameteur and professional race car drivers; and an autonomous, drifting Delorean named ‘MARTY.’

Chris Gerdes

Chris Gerdes is a Professor of Mechanical Engineering at Stanford University, Director of the Center for Automotive Research at Stanford (CARS) and Director of the Revs Program at Stanford. His laboratory studies how cars move, how humans drive cars and how to design future cars that work cooperatively with the driver or drive themselves. When not teaching on campus, he can often be found at the racetrack with students, instrumenting historic race cars or trying out their latest prototypes for the future. Vehicles in the lab include X1, an entirely student-built test vehicle, and Shelley, an Audi TT-S capable of turning a competitive lap time around the track without a human driver. Professor Gerdes and his team have been recognized with a number of awards including the Presidential Early Career Award for Scientists and Engineers, the Ralph Teetor award from SAE International and the Rudolf Kalman Award from the American Society of Mechanical Engineers.

Links

• Download mp3 (21.7 MB)
• Dynamic Design Lab
• Center for Automotive Research at Stanford
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Two reputable research resources are reporting that the robotics industry is growing more rapidly than expected. BCG (Boston Consulting Group) is conservatively projecting that the market will reach $87 billion by 2025; Tractica, incorporating the robotic and AI elements of the emerging self-driving industry, is forecasting the market will reach $237 billion by 2022.

Both research firms acknowledge that yesterday’s robots — which were blind, big, dangerous and difficult to program and maintain — are being replaced and supplemented with newer, more capable ones. Today’s new – and future robots will – have voice and language recognition, access to super-fast communications, data and libraries of algorithms, learning capability, mobility, portability and dexterity. These new precision robots can sort and fill prescriptions, pick and pack warehouse orders, sort, inspect, process and handle fruits and vegetables, plus a myriad of other industrial and non-industrial tasks, most faster than humans, yet all the while working safely along side them.

Boston Consulting Group (BCG)

Gaining Robotic Advantage, June 2017, 13 pages, free

BCG suggests that business executives be aware of ways robots are changing the global business landscape and think and act now. They see robotics-fueled changes coming in retail, logistics, transportation healthcare, food processing, mining and agriculture.

BCG cites the following drivers:

• Private investment in the robotic space has continued to amaze with exponential year-over-year funding curves and sensational billion dollar acquisitions.
• Prices continue to fall on robots, sensors, CPUs and communications while capabilities continue to increase.
• Robot programming is being transformed by easier interfaces, GUIs and ROS.
• The prospect of a self-driving vehicles industry disrupting transportation is propelling a talent grab and strategic acquisitions by competing international players with deep pockets.
• 40% of robotic startups have been in the consumer sector and will soon augment humans in high-touch fields such as health and elder care.

 BCG also cites the following example as an example of paying close attention to gain advantage:

“Amazon gained a first-mover advantage in 2012 when it bought Kiva Systems, which makes robots for warehouses. Once a Kiva customer, Amazon acquired the robot maker to improve the productivity and margins of its network of warehouses and fulfillment centers. The move helped Amazon maintain its low costs and expand its rapid delivery capabilities. It took five years for a Kiva alternative to hit the market. By then, Amazon had a jump on its rivals and had developed an experienced robotics team, giving the company a sustainable edge.”

Tractica

Robotics Market Forecast – June 2017, 26 pages, $4,200
Drones for Commercial Applications – June 2017, 196 pages, $4,200
AI for Automotive Applications – May 2017, 63 pages, $4,200
Consumer Robotics – May 2017, 130 pages, $4,200

The key story is that industrial robotics, the traditional pillar of the robotics market, dominated by Japanese and European robotics manufacturers, has given way to non-industrial robot categories like personal assistant robots, UAVs, and autonomous vehicles, with the epicenter shifting toward Silicon Valley, which is now becoming a hotbed for artificial intelligence (AI), a set of technologies that are, in turn, driving a lot of the most significant advancements in robotics. Consequently, Tractica forecasts that the global robotics market will grow rapidly between 2016 and 2022, with revenue from unit sales of industrial and non-industrial robots rising from $31 billion in 2016 to $237.3 billion by 2022.  The market intelligence firm anticipates that most of this growth will be driven by non-industrial robots.

Tractica is headquartered in Boulder and analyzes global market trends and applications for robotics and related automation technologies within consumer, enterprise, and industrial marketplaces and related industries.

General Research Reports

• Global autonomous mobile robots market, June 2017, 95 pages, TechNavio, $2,500
  TechNavio forecasts that the global autonomous mobile robots market will grow at a CAGR of more than 14% through 2021.
• Global underwater exploration robots, June 2017, 70 pages, TechNavio, $3,500
  TechNavio forecasts that the global underwater exploration robots market will grow at a CAGR of 13.92 % during the period 2017-2021.
• Household vacuum cleaners market, March 2017, 134 pages, Global Market Insights, $4,500
  Global Market Insights forecasts that household vacuum cleaners market size will surpass $17.5 billion by 2024 and global shipments are estimated to exceed 130 million units by 2024, albeit at a low 3.0% CAGR. Robotic vacuums show a slightly higher growth CAGR.
• Global unmanned surface vehicle market, June 2017, Value Market Research, $3,950
  Value Market Research analyzed drivers (security and mapping) versus restraints such as AUVs and ROVs and made their forecasts for the period 2017-2023.
• Innovations in Robotics, Sensor Platforms, Block Chain, and Artificial Intelligence for Homeland Security, May 2017, Frost & Sullivan, $6,950
  This Frost & Sullivan report covers recent developments such as co-bots for surveillance applications, airborne sensor platforms for border security, blockchain tech, AI as first responder, and tech for detecting nuclear threats.
• Top technologies in advanced manufacturing and automation, April 2017, Frost & Sullivan, $4,950
  This Frost & Sullivan report focuses on exoskeletons, metal and nano 3D printing, co-bots and agile robots – all of which are in the top 10 technologies covered.
• Mobile robotics market, December 2016, 110 pages, Zion Market Research, $4,199
  Global mobile robotics market will reach $18.8 billion by end of 2021, growing at a CAGR of slightly above 13.0% between 2017 and 2021.
• Unmanned surface vehicle (USV) market, May 2017, MarketsandMarkets, $5,650
  MarketsandMarkets forecasts the unmanned surface vehicle (USV) market to grow from $470.1 Million in 2017 to $938.5 Million by 2022, at a CAGR of 14.83%.
• Military/Civil UAS markets, May 2017, 608 pages, Teal Group
  The Teal Group’s 2016 world military market study estimates that UAV production will soar from current worldwide UAV production of $2.8 billion annually in 2016 to $9.4 billion in 2025, a 15.4% CAGR and that civil UAS production will soar from $2.6 billion worldwide in 2016 to $10.9 billion in 2025, a 15.4% CAGR.

Agricultural Research Reports

• Global agricultural robots market, May 2017, 70 pages, TechNavio, $2,500
  Forecasts the global agricultural robots market will grow steadily at a CAGR of close to 18% through 2021.
• Agriculture robots market, June 2017, TMR Research, $3,716
  Robots are poised to replace agricultural hands. They can pluck fruits, sow and reap crops, and milk cows. They carry out the tasks much faster and with a great degree of accuracy. This coupled with mandates on higher minimum pay being levied in most countries, have spelt good news for the global market for agriculture robots.
• Agricultural Robots, December 2016, 225 pages, Tractica, $4,200
  Forecasts that shipments of agricultural robots will increase from 32,000 units in 2016 to 594,000 units annually in 2024 and that the market is expected to reach $74.1 billion in annual revenue by 2024. Report, done in conjunction with The Robot Report, profiles over 165 companies involved in developing robotics for the industry.

Bottom Line

The disparity between the projections of these research reports is wide but the CAGRs are mostly all double digit. It is easy to conclude as BCG did – that the robotics industry is growing faster than expected

Future robots will work side by side with humans, just as they do today.
Credit: AP Photo/John Minchillo

by Thomas Kochan, MIT Sloan School of Management and Lee Dyer, Cornell University

The technologies driving artificial intelligence are expanding exponentially, leading many technology experts and futurists to predict machines will soon be doing many of the jobs that humans do today. Some even predict humans could lose control over their future.

While we agree about the seismic changes afoot, we don’t believe this is the right way to think about it. Approaching the challenge this way assumes society has to be passive about how tomorrow’s technologies are designed and implemented. The truth is there is no absolute law that determines the shape and consequences of innovation. We can all influence where it takes us.

Thus, the question society should be asking is: “How can we direct the development of future technologies so that robots complement rather than replace us?”

The Japanese have an apt phrase for this: “giving wisdom to the machines.” And the wisdom comes from workers and an integrated approach to technology design, as our research shows.

Lessons from history

There is no question coming technologies like AI will eliminate some jobs, as did those of the past.

More than half of the American workforce was involved in farming in the 1890s, back when it was a physically demanding, labor-intensive industry. Today, thanks to mechanization and the use of sophisticated data analytics to handle the operation of crops and cattle, fewer than 2 percent are in agriculture, yet their output is significantly higher.

But new technologies will also create new jobs. After steam engines replaced water wheels as the source of power in manufacturing in the 1800s, the sector expanded sevenfold, from 1.2 million jobs in 1830 to 8.3 million by 1910. Similarly, many feared that the ATM’s emergence in the early 1970s would replace bank tellers. Yet even though the machines are now ubiquitous, there are actually more tellers today doing a wider variety of customer service tasks.

So trying to predict whether a new wave of technologies will create more jobs than it will destroy is not worth the effort, and even the experts are split 50-50.

It’s particularly pointless given that perhaps fewer than 5 percent of current occupations are likely to disappear entirely in the next decade, according to a detailed study by McKinsey.

Instead, let’s focus on the changes they’ll make to how people work.

It’s about tasks, not jobs

To understand why, it’s helpful to think of a job as made up of a collection of tasks that can be carried out in different ways when supported by new technologies.

And in turn, the tasks performed by different workers – colleagues, managers and many others – can also be rearranged in ways that make the best use of technologies to get the work accomplished. Job design specialists call these “work systems.”

One of the McKinsey study’s key findings was that about a third of the tasks performed in 60 percent of today’s jobs are likely to be eliminated or altered significantly by coming technologies. In other words, the vast majority of our jobs will still be there, but what we do on a daily basis will change drastically.

To date, robotics and other digital technologies have had their biggest effects on mostly routine tasks like spell-checking and those that are dangerous, dirty or hard, such as lifting heavy tires onto a wheel on an assembly line. Advances in AI and machine learning will significantly expand the array of tasks and occupations affected.

Creating an integrated strategy

We have been exploring these issues for years as part of our ongoing discussions on how to remake labor for the 21st century. In our recently published book, “Shaping the Future of Work: A Handbook for Change and a New Social Contract,” we describe why society needs an integrated strategy to gain control over how future technologies will affect work.

And that strategy starts with helping define the problems humans want new technologies to solve. We shouldn’t be leaving this solely to their inventors.

Fortunately, some engineers and AI experts are recognizing that the end users of a new technology must have a central role in guiding its design to specify which problems they’re trying to solve.

The second step is ensuring that these technologies are designed alongside the work systems with which they will be paired. A so-called simultaneous design process produces better results for both the companies and their workers compared with a sequential strategy – typical today – which involves designing a technology and only later considering the impact on a workforce.

An excellent illustration of simultaneous design is how Toyota handled the introduction of robotics onto its assembly lines in the 1980s. Unlike rivals such as General Motors that followed a sequential strategy, the Japanese automaker redesigned its work systems at the same time, which allowed it to get the most out of the new technologies and its employees. Importantly, Toyota solicited ideas for improving operations directly from workers.

In doing so, Toyota achieved higher productivity and quality in its plants than competitors like GM that invested heavily in stand-alone automation before they began to alter work systems.

Similarly, businesses that tweaked their work systems in concert with investing in IT in the 1990s outperformed those that didn’t. And health care companies like Kaiser Permanente and others learned the same lesson as they introduced electronic medical records over the past decade.

Each example demonstrates that the introduction of a new technology does more than just eliminate jobs. If managed well, it can change how work is done in ways that can both increase productivity and the level of service by augmenting the tasks humans do.

Worker wisdom

But the process doesn’t end there. Companies need to invest in continuous training so their workers are ready to help influence, use and adapt to technological changes. That’s the third step in getting the most out of new technologies.

And it needs to begin before they are introduced. The important part of this is that workers need to learn what some are calling “hybrid” skills: a combination of technical knowledge of the new technology with aptitudes for communications and problem-solving.

Companies whose workers have these skills will have the best chance of getting the biggest return on their technology investments. It is not surprising that these hybrid skills are now in high and growing demand and command good salaries.

None of this is to deny that some jobs will be eliminated and some workers will be displaced. So the final element of an integrated strategy must be to help those displaced find new jobs and compensate those unable to do so for the losses endured. Ford and the United Auto Workers, for example, offered generous early retirement benefits and cash severance payments in addition to retraining assistance when the company downsized from 2007 to 2010.

Examples like this will need to become the norm in the years ahead. Failure to treat displaced workers equitably will only widen the gaps between winners and losers in the future economy that are now already all too apparent.

In sum, companies that engage their workforce when they design and implement new technologies will be best-positioned to manage the coming AI revolution. By respecting the fact that today’s workers, like those before them, understand their jobs better than anyone and the many tasks they entail, they will be better able to “give wisdom to the machines.”

Thomas Kochan, Professor of Management, MIT Sloan School of Management and Lee Dyer, Professor Emeritus of Human Resource Studies and Research Fellow, Center for Advanced Human Resource Studies (CAHRS), Cornell University

This article was originally published on The Conversation. Read the original article.

China has rapidly become a global leader in robotics and automation. 2016 annual sales of industrial robots reached the highest level ever for any single country: 87,000 units (up 27% from 2015) and China’s stock of industrial robots is now, at 340,000 units, also the highest total in the world. while Chinese robot manufacturers increased their market share to 31% (up 120% from 2015).

The International Federation of Robotics (IFR), which provided these figures, is forecasting that “from 2018 to 2020, a sales increase between 15 and 20 percent on average per year is possible for industrial robots.” And these projections don’t include service robots for professional and B2B use, and personal use such as toys, drones, mobile gofers, guides, home assistants, and consumer products like robotic vacuums and floor and window cleaners.

Outlook for 2017

According to a report released by China Robot Industry Alliance (CRIA) at the big World Robot Conference in August in Beijing and reported by China Daily, China’s industrial robot market is expected to reach $4.22 billion in 2017 representing more than 110,000 new industrial robots.

At the same press conference, CRIA also reported that China’s service robot market will reach $1.32 billion this year, up 28% percent from 2015.

Outlook to 2020

The main drivers for the growth of the use of industrial robots in China are the electrical and electronics industry followed by general handling, welding and the auto industry. This broad and expanding demand is expected to continue as major contract manufacturers start and/or continue to automate their production. A further driving factor is China’s growing consumer market for all kinds of consumer goods.

According to the ten-year national plan “Made in China 2025,” the Chinese government wants to transform China from a low-cost labor-intensive manufacturing giant into a technology-based world manufacturing power. The plan includes strengthening Chinese robot suppliers and further increasing their market shares in China and abroad.

Shanzhai

Shenzhen is the Silicon Valley of technology and hardware for China. Things get made FAST. All kinds of ‘things.’ The can-make attitude in Shenzhen is being duplicated around China thus it is important to know what goes on, why it happens in Shenzhen, why it happens so fast, and what they think about patents, intellectual property and Western companies.

Another factor (driver) in China’s relentless push toward automation and robotics is this factoid: In 2016, China’s mobile payments hit $5.5 trillion, roughly 50 times the size of America’s $112 billion market, according to consulting firm iResearch. Chinese are adopting cashless and e-commerce methods at a rate significantly faster than the rest of the world.

WIRED Video produced an hour-long documentary describing the process, the people, and ‘Shanzhai,’ the evolving philosophy of copycat manufacturing, and attempts to put a positive spin on patent avoidance and what many Westerners call stealing, plus the speed of production for adequate profits (as opposed to massive profits). It is a worthwhile and very informative investment of an hour of your time.

Last week I was talking to one lead engineer of a Singapore company which is building a benchmarking system for robot solutions. Having seen my presentation at ROSCON2016 about robot benchmarking, he asked me how I would benchmark solutions that are non-ROS compatible. I said that I wouldn’t dedicate time to benchmark solutions that are not ROS-based. Instead, I suggested I would use the time to polish the ROS-based benchmarking and suggest that vendors adopt that middleware in their products.

Benchmarks are necessary and they need standards

Benchmarks are necessary to improve any field. By having a benchmark, different solutions to a single problem can be compared and hence a direction for improvement can be traced. Currently, robotics lacks such benchmarking system.

I strongly believe that to create a benchmark for robotics we need a standard at the level of programming.

By having a standard at the level of programming, manufacturers can build their own hardware solutions at will, as long as they are programmable with the programming standard. That is the approach taken by devices that can be plugged into a computer. Manufacturers create the product on their own terms, and then provide a Windows driver that allows any computer in the world (that runs Windows) to communicate with the product. Once this computer-to-product communication is made, you can create programs that compare the same type of devices from different manufacturers for performance, quality, noise, whatever your benchmark is trying to compare.

You see? Different types of devices, different types of hardware. But all of them can be compared through the same benchmarking system that relies on the Windows standard.

Software development for robots also needs standards

The need for standards is not only required for comparing solutions but also to speed robotics development. By having a robotics standard, developers can concentrate on building solutions that do not have to be re-implemented whenever the robot hardware changes. Actually, given the middleware structure, developers can disassociate enough from the hardware that they can almost spend 100% of their time in the software realm, while still developing code for robots.

We need the same type of standard for robotics. We need a kind of operating system that allows us to compare different robotics solutions. We need the Windows of the PCs, the Android of the phones, the CAN of the buses…

A few standard proposals and a winner

But you already know that. I’m not the first one to state this. Actually, many people have already tried to create such a standard. Some examples include Player, ROS, YARP, OROCOS, Urbi, MIRA or JdE Robot, to name a few.

Personally, I actually don’t care which standard is used. It could be ROS, it could be YARP, or it could be any other that still has not been created. The only thing I really care about is that we  adopt a standard as soon as possible.

And it looks like the developers have decided. Robotics developers prefer ROS as their common middleware to program robots.

No other middleware for robotics has had such a large adoption. Some data about it:

	ROS	YARP	OROCOS
Number of Google pages:	243.000	37.000	42.000
Number of citations if the paper describing the middleware:	3.638	463	563
Alexa ranking:	14.118	1.505.000	668.293

Note 1: Only showing the current big three players.

Note 2: Very simple comparison. Difficult to compare in other terms since data is not available.

Note 3: Data measured in August 2017. May vary at the time you are reading this. Links provided on the numbers themselves, so you can check yourself.

This is not only the feeling that we, roboticists, have. The numbers also indicate that ROS is becoming the standard for robotics programming.

Why ROS?

The question is then, why has ROS emerged on top of all the other possible contestants. None of them is worst than ROS in terms of features. Actually you can find some feature in all the other middlewares that outperform ROS. If that is so, why or how has ROS achieved the status of becoming the standard ?

A simple answer from my point of view: excellent learning tutorials and debugging tools.

Here there is a video where Leila Takayama, early developer of ROS, explains when she realized that the key for having ROS used worldwide would be to provide tools that simplify the reuse of ROS code. None of the other projects have such a set of clear and structured tutorials. In addition, few of the other middlewares provide debugging tools for their packages. The lack of these two essential aspects is preventing new people from using their middlewares (even if I understand the developers of OROCOS and YARP for not providing it… who wants to write tutorials or build debugging tools… nobody! ? )

Additionally, it is not only about tutorials and debugging tools. ROS creators also provide a good system of managing packages. The result is that developers worldwide could use others packages in a (relatively) easy way. This created an explosion in ROS packages available, providing off-the-shelf almost anything for your brand new ROSified robot.

Now, the impressive rate at which contributions to the ROS ecosystem are made makes it almost unstoppable in terms of growing.

What about companies?

At the beginning, ROS was mostly used by students at Universities. However, as ROS becomes more mature and the number of packages increases, companies are realizing that adopting ROS is also good for them because they will be able to use code developed by others. On top of that, it will be easier for them to hire new engineers who already know the middleware (otherwise they would need to teach the newcomers their own middleware).

As a result, many companies have jumped onto the ROS train, developing from scratch their products to be ROS compatible. Examples include Robotnik, Fetch Robotics, Savioke, Pal Robotics, Yujin Robots, The Construct, Rapyuta Robotics, Erle Robotics, Shadow Robot or Clearpath, to name a few of the sponsors of the next ROSCON ? . Creating their ROS-compatible products, they decreased their development time by several orders of magnitude.

To bring things further, two Spanish companies have revolutionised the standardization of robotics products using ROS middleware: on one side, Robotnik has created the ROS Components shop. A shop where anyone can buy ROS compatible devices, starting from mobile bases to sensors or actuators. On the other side, Erle Robotics (now Acutronic Robotics) is in the process of developing Hardware ROS. The H-ROS is a standardized software and hardware infrastructure to easily create reusable and reconfigurable robot hardware parts. ROS is enabling hardware standarization too, but this time driven by companies, not research! That must mean something…

Finally, it looks like industrial robot manufacturers have understood the value that a standard can provide to their business. Even if they do not make their industrial robots ROS-enabled from the start, they are adopting ROS Industrial  a flavour of ROS, which allows them to ROSify their industrial robots and re-use all the software created for manipulators in the ROS ecosystem.

But are all companies jumping onto the ROS train? Not all of them!

Some companies like Jibo, Aldebaran or Google still do not rely on ROS for their robot programming. Some of them rely on their own middleware created before the existence of ROS  (that is the case of Aldebaran). Some others, though, are creating their own middleware from scratch. Their reasons: they do not believe ROS is good, they have already created a middleware, or do not want to develop their products dependent on the middleware of others. Those companies have very fair reasons to go their way. However, will that make them competitive? (if we have to judge from history, mobiles, VCRs, the answer may be no).

So is ROS the standard for programming robots?

That question is still too soon to be answered. It looks like it is becoming the standard, but many things can change. It is unlikely that another middleware takes the current title from ROS, but it may happen. There could be a new player that wipes ROS from the map (maybe Google will release its middleware to the public – like they did with Android – and take the sector by storm?).

Still, ROS has its problems, like a lack of security or the instability of some important packages. Even if the OSRF group are working hard to build a better ROS system (for instance ROS2 is in beta phase with many root improvements), some hard work is still required for some basic things (like the ROS controllers for real robots).

Given those numbers, at The Construct we believe that ROS IS THE STANDARD (that is why we are devoted to creating the best ROS learning tutorials of the world). Actually, it was thanks to this standardization that two Barcelona students were able to create an autonomous robot product for coffee shops in only three months with zero knowledge of robotics (see Barista robot).

This is the future, and it is good. In this future, thanks to standards, almost anyone will be able to build, design and program their own robotics product, similar to how PC stores are building computers today.

So my advice, as I said to the Singapore engineer, is to bet on ROS. Right now, it is the best option for a robotics standard.

Robotic food manufacturing is a rising trend in the food industry. The value of the global food automation industry is expected to rise to $2.5 billion by 2022. In this article, we introduce six robotic applications in food processing. As we discussed in our previous article on the food industry, food manufacturing can be separated into two stages: primary food processing and secondary food processing.

Top 3 Robotic Applications in Primary Food Processing

Primary processing involves handling raw food products, which are cleaned, sorted, chopped, packaged, etc. Some foods, like raw vegetables, will only undergo primary processing before they are packaged for the consumer. Other foods will undergo secondary processing before packaging.

Up until quite recently, robotic processing at this stage has been limited or non-existent. Raw foods are variable in size, weight and shape. This makes it difficult for robots to handle them. However, recent developments in sensing and soft gripping has made it possible for robots to handle many raw foods.

1. Robotic Butchery

Butchery is a very difficult task to automate. Every animal carcass is different. A skilled butcher will adapt each cut to the shape and position of bones and meat. Some butchery tasks are simpler to automate than others. For example, high-volume chicken leg deboning is an established part of the meat processing industry.

Beef butchery has traditionally been very difficult to automate. Recently, beef manufacturer JBS has started looking for ways to introduce robots into their factories. Parts of the process are very dangerous for human workers. Rib cutting, for example, involves operating a high-speed circular saw for several hours. JBS has managed to automate this action using robot manipulators and various vision sensors. The application has improved safety and product consistency.

2. Fruit and Vegetable Pick and Place

Fruits and vegetables are challenging to handle with a robot due to their variable sizes and shapes. They also require delicate handling to avoid damage. For these reasons they have traditionally been handled by human workers. However, recent developments in gripping technologies look to change all that. Soft Robotics Inc has introduced a flexible gripper which can handle very delicate foods, even individual lettuce leaves!

https://youtu.be/3UE3Wi-NEno

Another example is Lacquey’s gripper, which uses paddles to lift soft fruits and vegetables.

3. Robotic Cutting and Slicing

Some cutting and slicing tasks are easy to automate. For example, even kitchen food processors can slice vegetables into uniform shapes. Robots are not needed for this type of simple automation.

For more advanced cutting and slicing, however, the food industry has relied on human workers but robotics is starting to make its way into the industry. Fish cutting, for example, involves detecting and removing defects from the fish as well as cutting fillets to uniform shapes and sizes.

Top 3 Robotic Applications in Secondary Food Processing

Secondary processing involves handling products which have already undergone primary processing. Robots have been used for several applications for a long time, particularly pick and place. However, recent developments have opened the door to even more advanced applications.

1. Product Pick and Place

You may be familiar with the high speed delta robots which are used to move food products around a production line. If not, here is a video:

https://youtu.be/xBBryczPFf4

This is an example of secondary processing pick and place. It is distinct from the vegetable pick and place mentioned above because the products are more uniform in shape and size. Uniform foods are much easier to handle robotically, so this application has been available in the food industry for many years.

2. Cake Decorating

One impressive application is robotic cake decoration. This involves using a robotic arm much like a 3D printer to pipe icing onto a cake. The Deco-Bot from Unifiller can pipe hand-drawn decorations onto cakes on a moving conveyor. 

Cake cutting can also be done robotically, like the Katana waterjet cutting robot which can cut out intricate shapes in cakes using high pressure water.

3. Pizza Making

Artisan food producers sometimes worry that adding robots their process will make their products less “hand-made.” However, Silicon Valley pizza producer Zume is showing how robots can be produced to look like they have the human touch. Their pizzeria uses two robots: a delta robot to spread the tomato sauce and a ABB manipulator to tend the pre-baking ovens. While their system is far from fully automated, their goal is to make the pizza delivery industry a labor-free business.

Finally… Washing Up!

Contaminated food causes 48 million people in the USA to become sick annually. Robotic food processing has the potential to reduce this, by removing human workers from parts of the process, but this is only possible if the robots themselves do not cause contamination.

One of the more challenging issues for food automation is the fact that every piece of machinery must be thoroughly cleaned to avoid contamination. Robot manufacturers have been working to make their robot casings smoother, with better ingress ratings and no loose wires. This allows them to be thoroughly washed down at the end of each cycle.

In this system from JMP Automation, the two robots wash down the workcell with high powered water, and even wash down each other:

https://youtu.be/87fIbRWbyII

Instead of worrying so much about robots taking away jobs, maybe we should worry more about wages being too low for robots to even get a chance. Seasonal labor for harvesting agricultural products, particularly fruits and vegetables, is dependent on human labor from a diminishing universe of willing workers.

Robots that can supplement or replace human workers in the harvesting process are being developed and tested in startups and academia, but almost all are not yet ready for prime time.

In a NY Times article written by Neil Irwin entitled Rethinking Low Productivity, productivity growth has been on a downward path since the Financial Crisis. Irwin, who writes about economic trends, asks whether the downward trend is the cause of low growth, or the result, a troubling question in the dynamics of the agriculture industry.

“Inventors and business innovators are always developing better ways to do things, but it takes a labor shortage and high wages to coax firms to deploy the investment it takes to actually put those innovations into widespread use.”

Those two phenomena are happening today in the U.S. agricultural industry. Labor rates are rising while willing laborers are diminishing — yet farmers are not yet investing in robotics. In fact, they are accommodating to the labor crisis by planting less and changing the crops they grow to be less labor intensive.

J.W. Mason, the author of a paper by the Roosevelt Institute cited as the basis for the thesis of Irwin’s article, says:

“If the labor market tightens and wages rise, that will be the impetus to get companies to consider more big-ticket innovations that generate productivity growth so that we don’t perpetuate the present conundrum in which both arguments cannot be true:

“On Mondays and Wednesdays, economists argue that wages are low because robots are taking people’s jobs.

On Tuesdays and Thursdays, it’s that we can’t have wages rise because productivity growth is low.”

This situation cannot continue without radical changes else we will become buyers of foreign fruits and vegetables paying prices set by foreign providers and dependent on them for availability and quality.

A number of factors are causing a reset in the ag industry in addition to the declining availability of farm workers: the challenges and complexities of employing and retaining farm labor; the rising cost of farm workers; changing farmlands; climate change; the growth of indoor farming; and the broader automation of the agriculture industry. All are propelling farmers to make changes in how they farm (or change downward the product mix they farm (which is often a path toward failure)). Market challenges for the sector include unclear value propositions, limited awareness of robotic systems among growers, insufficient robotic solutions, the difficulty of matching human-like dexterity with machines, fragmented technology development, and weak support.

Tractica, in their report on agricultural robotics, forecast an optimistic resolution to this temporary conundrum in the very near term and that that shipments of agricultural robots will increase significantly in the years ahead, rising from 32,000 units in 2016 to 594,000 units annually by 2024, by which time the market is expected to reach $74 billion in annual revenue. Certainly the time is ripe and, like Mason said, “Both arguments can’t be true.”

Watch the video below to see these inventive teams – and their robots – in action.

Congratulations to this year’s winners from the Australian Centre for Robotic Vision. See the full results here.

The first robot, a Universal Robots UR5, takes a glass out of an industrial dishwasher rack. The challenge here is, that the glasses are placed upside down in the rack and have to be turned. Furthermore, there are two types of glasses – one for long drinks and one for shots like ‘whisky on the rocks’. The problem was mainly solved with the design of custom gripper fingers. They made it possible to grasp, turn and release the different types of glasses without an intermediate manipulation step. Also, some rubber bands increased the friction and made it possible to let the glass slide down smoothly on the belt. After releasing the glass, the glass tracking started to determine the exact pose.

To get to know the exact position of the glass on the conveyor belt an image processing pipeline calculated its pose. Especially, the transparency of the glass itself made it difficult to detect them reliably at every position. Otherwise the ice cubes or the liquor where not poured into the glass, but off target.

While the glass was placed on the center of the conveyor belt by the first robot, the second robot, a Schunk LWA 4P, started to fill its shovel with ice cubes out of an ice box. It is tricky as the ice cubes stick together after some time and they also change their form by melting. Again, a custom designed gripper guaranteed to get the right amount of ice cubes in each glass.

After ice was added the next step was to prepare the liquor. In total, there were four different kinds of shots – gin, whisky, rum and vodka. All of the liquors where in their original bottles and the third robot, a KUKA KR10 in combination with a Robotiq Three-Finger-Gripper, grasped them precisely. A special liquid nozzle made sure that only 4cl of liquor were poured in each glass after the robot placed the bottle opening above the glass. Pouring while following the movement of the glass made this process independent of liquid level or bottle type.

At the end of the first conveyor belt the fourth robot, again a UR5 with a Schunk PG70 gripper, waited for the arrival of the glass. If the guest just ordered a shot the glass was moved onto the second conveyor belt. Otherwise one of the soft drinks was added. Apart from sparkling and tap water, the taping system provided coke, tonic water, bitter lemon and orange juice. When the right amount of soft drink was added to the drink, the long drink glass was also placed on the other belt.

Only one part missing: The straw. While the fourth robot prepared the drink the fifth and biggest robot, a Universal Robots UR10 and a Weiss WSG-25 gripper, started to get a straw out of the straw dispenser standing next to it. After picking one, the arm moved to its waiting pose above the conveyor belt until the glass arrived. Again, custom designed gripper fingers made it possible to pick a straw out of the box as well as grasping the glass filled with liquids.
When the glass was within reach, the gripper released the straw into the glass and the arm approached nicely towards the glass to grasp it and place it on an interactive table. This was used to show the placed orders as well as the current drink making progress.

All the robots had to work synchronized, with almost no free space around them and close distance to the guests. The Robot Operating System (ROS) made it possible, to control all different kind of robotic arms and grippers within one high-level controller. Each robot station was triggered separately to increase the robustness and also the possibilities of extending the demonstrator for future parties.

The Cocktail Bot 4.0 was created and programmed by a small team of researchers from the FZI Research Center for Information Technologies in Karlsruhe, Germany.

By Jeff Morgan, Trinity College Dublin

Robots have been taking our jobs since the 1960s. So why are politicians and business leaders only now becoming so worried about robots causing mass unemployment?

It comes down to the question of what a robot really is. While science fiction has often portrayed robots as androids carrying out tasks in the much the same way as humans, the reality is that robots take much more specialised forms. Traditional 20th century robots were automated machines and robotic arms building cars in factories. Commercial 21st century robots are supermarket self-checkouts, automated guided warehouse vehicles, and even burger-flipping machines in fast-food restaurants.

Ultimately, humans haven’t become completely redundant because these robots may be very efficient but they’re also kind of dumb. They do not think, they just act, in very accurate but very limited ways. Humans are still needed to work around robots, doing the jobs the machines can’t and fixing them when they get stuck. But this is all set to change thanks to a new wave of smarter, better value machines that can adapt to multiple tasks. This change will be so significant that it will create a new industrial revolution.

Industry 4.0

This era of “Industry 4.0” is being driven by the same technological advances that enable the capabilities of the smartphones in our pockets. It is a mix of low-cost and high-power computers, high-speed communication and artificial intelligence. This will produce smarter robots with better sensing and communication abilities that can adapt to different tasks, and even coordinate their work to meet demand without the input of humans.

In the manufacturing industry, where robots have arguably made the most headway of any sector, this will mean a dramatic shift from centralised to decentralised collaborative production. Traditional robots focused on single, fixed, high-speed operations and required a highly skilled human workforce to operate and maintain them. Industry 4.0 machines are flexible, collaborative and can operate more independently, which ultimately removes the need for a highly skilled workforce.

For large-scale manufacturers, Industry 4.0 means their robots will be able to sense their environment and communicate in an industrial network that can be run and monitored remotely. Each machine will produce large amounts of data that can be collectively studied using what is known as “big data” analysis. This will help identify ways to improve operating performance and production quality across the whole plant, for example by better predicting when maintenance is needed and automatically scheduling it.

For small-to-medium manufacturing businesses, Industry 4.0 will make it cheaper and easier to use robots. It will create machines that can be reconfigured to perform multiple jobs and adjusted to work on a more diverse product range and different production volumes. This sector is already beginning to benefit from reconfigurable robots designed to collaborate with human workers and analyse their own work to look for improvements, such as BAXTER, SR-TEX and CareSelect.

While these machines are getting smarter, they are still not as smart as us. Today’s industrial artificial intelligence operates at a narrow level, which gives the appearance of human intelligence exhibited by machines, but designed by humans.

What’s coming next is known as “deep learning”. Similar to big data analysis, it involves processing large quantities of data in real time to make decisions about what is the best action to take. The difference is that the machine learns from the data so it can improve its decision making. A perfect example of deep learning was demonstrated by Google’s AlphaGo software, which taught itself to beat the world’s greatest Go players.

The turning point in applying artificial intelligence to manufacturing could come with the application of special microchips called graphical processing units (GPUs). These enable deep learning to be applied to extremely large data sets at extremely fast speeds. But there is still some way to go and big industrial companies are recruiting vast numbers of scientists to further develop the technology.

Impact on industry

As Industry 4.0 technology becomes smarter and more widely available, manufacturers of any size will be able to deploy cost-effective, multipurpose and collaborative machines as standard. This will lead to industrial growth and market competitiveness, with a greater understanding of production processes leading to new high-quality products and digital services.

Exactly what impact a smarter robotic workforce with the potential to operate on its own will have on the manufacturing industry, is still widely disputed. Artificial intelligence as we know it from science fiction is still in its infancy. It could well be the 22nd century before robots really have the potential to make human labour obsolete by developing not just deep learning but true artificial understanding that mimics human thinking.

Ideally, Industry 4.0 will enable human workers to achieve more in their jobs by removing repetitive tasks and giving them better robotic tools. In theory, this would allow us humans to focus more on business development, creativity and science, which it would be much harder for any robot to do. Technology that has made humans redundant in the past has forced us to adapt, generally with more education.

But because Industry 4.0 robots will be able to operate largely on their own, we might see much greater human redundancy from manufacturing jobs without other sectors being able to create enough new work. Then we might see more political moves to protect human labour, such as taxing robots.

Again, in an ideal scenario, humans may be able to focus on doing the things that make us human, perhaps fuelled by a basic income generated from robotic work. Ultimately, it will be up to us to define whether the robotic workforce will work for us, with us, or against us.

This article was originally published on The Conversation. Read the original article.

The K5 security robot fell into a fountain in Washington, D.C.

July 17, 2017 – July 23, 2017

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News

A U.S. drone strike in Afghanistan is reported to have mistakenly killed 15 Afghan soldiers. In a statement, Afghanistan’s Ministry of Defense reported that the strike hit a security outpost in Helmand province. (Voice of America)

The U.K. Department of Transport is developing regulations that would implement a drone registration program, safety courses for drone owners, and more extensive geo-fencing to keep drones out of restricted areas. According to the BBC, it is not yet clear when the new rules will go into effect.

China announced plans to advance the development of artificial intelligence. The State Council of the People’s Republic of China released a plan to grow AI-related industries into a $59.07 billion sector by 2025. (Reuters)

Canada’s transportation safety agency issued an update to its drone regulations. The update relaxes key provisions for recreational and commercial drone users. (CTV News)

Commentary, Analysis, and Art

In testimony before the U.S. Senate, Air Force General Paul Selva argued that autonomous weapons should never be allowed to decide whether or not to take a human life. (Breaking Defense)

In a discussion at the National Governors Association annual meeting, Tesla CEO Elon Musk argued that artificial intelligence is a “fundamental existential risk for human civilization.” (Real Clear Politics)

At Wired, Tom Simonite argues that Musk’s comments are a distraction from the real problems of artificial intelligence.

A report by the Harvard Belfer Center for Science and International Affairs considers how artificial intelligence could revolutionize warfare. (Wired)

At the Wall Street Journal, Jeremy Page and Paul Sonne look at how China is stepping up exports of drones to U.S. allies.

At Scout Warrior, David Hambling looks at how separatists in eastern Ukraine are arming small drones with grenades and other ordnance.

A NASA study found that noise from drones is more irritating to people than noise from cars. (New Scientist)

At Popular Mechanics, Eric Tegler looks at how the U.S. Air Force is testing equipment intended for the Reaper drone on a World War II-era Douglas DC-3.

At War on the Rocks, Gregory C. Allen considers how animals in nature inspire the design of military robots.

Also at War on the Rocks, Scott Cuomo argues that the Marine Corps needs a persistent aerial surveillance and strike drone like the MQ-9 Reaper.

At the Intercept, Robert Trafford and Nick Turse write that Cameroonian forces used a drone base to torture suspected members of Boko Haram.

CNN looks at how Agadez, a city in northern Niger where the U.S. is building a drone base, is an unstable “tinderbox.”

At the Motley Fool, Rich Smith offers suggestions on drone manufacturers to invest in.

Know Your Drone

Researchers at MIT are developing small power-efficient computer chips that could be used to build highly autonomous micro-drones. (The Drive)

An Australian student has developed a drone that is capable of flying for longer and at much higher speeds than other consumer systems. (ABC News)

Airbus Defense and Space conducted a test flight of a subscale model of the Sagitta stealth drone that it is developing with a group of German research institutes. (Aviation Week)

A team at the Singapore University of Technology & Design has developed a vertical take-off drone that transitions to horizontal flight by turning its rotors into wings. (The Verge)

A team at Stanford University is developing a wormlike robot that can move by expanding in size. (Science Robotics)

The Chinese military is developing what appears to be an 8×8 unmanned supply truck. (IHS Jane’s Defence Weekly)

Russian defense firm Kronstadt unveiled the concept for its Orion-E, a military drone that will be about the size of the MQ-1 Predator. (FlightGlobal)

Meanwhile, Russian Helicopters unveiled the VRT3000, a co-axial rotor reconnaissance helicopter drone. (Shephard Media)

Estonian firm Threod Systems is developing a new variant of its Stream tactical military drone. (IHS Jane’s International Defence Review)

Researchers at MIT have developed a small robot that can swim through water pipes searching for leaks. (Dezeen)

YouTube channel Make it Extreme published a video showing how one can build a DIY counter-drone net gun. (Popular Mechanics)

Estonian Startup Marduk Technologies plans to begin testing its Shark counter-drone system with the Estonian military in August or September. (IHS Jane’s International Defence Review)

The U.S. Navy issued a draft Request for Proposals detailing some of the characteristics of its planned MQ-25A Stingray refueling drone. (USNI News)

Israeli firm GPSdome has developed a jam-resistant GPS system for drones. (C4ISRNET)

Ukraine-based defense firm Infocom revealed new details about its Laska armed unmanned ground vehicle. (IHS Jane’s International Defence Review)

Drones at Work

Singapore has offered the Philippines drones and other military equipment for operations against Islamist militant groups. (Reuters)

Police departments in Dorset, Cornwall, and Devon in the U.K. have been using drones to track reckless motorcyclists. (The Drive)

The town of Deadwood in South Dakota has approved an ordinance restricting the use of drones in the city. (Black Hills Pioneer)

Investigators have concluded that a mid-air collision in Australia that was thought to have been caused by a drone was actually caused by a bat. (ABC News)

In a test, the U.S. Navy used its Laser Weapon System to shoot down a drone. (CNN)

A remotely operated robot exploring the interior of Fukushima’s reactor 3 appears to have discovered objects that could be fuel debris. (Japan Times)

The U.S. Air Force has established a program to teach coalition forces how to react to adversary drones on the battlefield. (Unmanned Systems Technology)

A drone flying near the scene of a car crash in Avonport, Canada delayed the departure of a helicopter that was airlifting a patient to hospital. (CBC)

The police department of Harvey County, Kansas found a missing 91-year-old man by using a drone. (KWCH)

Canada’s OEX Recovery group will use a Kraken unmanned undersea vehicle to search for the remains of several subscale prototype jets that crashed into Lake Ontario in the 1950s. (Unmanned Systems Technology)

The U.S. Navy Special Warfare Command is testing a vehicle-mounted SkySafe counter-drone system. (GCN)

LG Electronics has begun a live trial of a series of cleaning and guide robots at Incheon International Airport in South Korea. (ZDNet)

Meanwhile, a Knightscope security robot fell into a fountain while patrolling an office building in Washington, D.C. (CNN)

A drone crashed while racing a Formula E electric race car during an event in Brooklyn. (The Drive)

Industry Intel

Counter-drone company SkySafe raised $11.5 million in a Series A funding round led by Andreessen Horowitz. (TechCrunch)

The U.S. Navy awarded Hydroid a $27.3 million contract modification for the Mk 18 Kingfish family of unmanned undersea vehicles. (DoD)

The U.S. Coast Guard awarded General Dynamics Mission Systems a $29,610 contract for an unmanned underwater vehicle. (FBO)

The U.S. Air Force awarded Engility Corporation a contract for “autonomous collaborative vehicle research and development.” (FBO)

The U.S. Navy awarded Insitu a $39,810 contract for software for the RQ-21A reconnaissance drone. (FBO)

The U.S. Army awarded Leonardo DRS and Moog a $16 million contract to develop a vehicle-mounted counter-drone system. (UPI)

The U.S. Department of Homeland Security awarded General Atomics Aeronautical Systems a $3.9 million contract for UAS operational support and maintenance. (USASpending)

The Defense Advanced Research Projects Agency awarded BAE Systems a $4.6 million contract under the Mobile Offboard Clandestine Communications Approach program. (USNI News)

Georgia’s Innovation Fund Tiny Grant program awarded the Long Cane Middle School an $8,000 grant to develop a curriculum on drones. (The Journal)

U.S. drone firm Measure will offer new inspection services to the solar energy industry. (Unmanned Systems Technology)

Propeller Aero will begin distributing Trimble’s Connected Site, a platform for analysis data from drones. (Unmanned Systems Technology)

Solent Local Enterprise Partnership awarded BAE Systems a $593,871 grant to design a testing site for autonomous systems in the U.K. (Inside Unmanned Systems)

The U.K.’s Ministry of Defense awarded Inzpire Limited a contract to help train pilots of the new General Atomics Protector drone. (Inzpire)

Iran Aircraft Manufacturing Industries announced that it will begin marketing the Hamaseh surveillance and strike drone to international customers. (FlightGlobal)

Israel Aerospace Industries will offer India an agreement to produce the Heron TP domestically. (FlightGlobal)

Israeli drone company Aeronautics will acquire an unnamed U.S. firm for $6 million. (IHS Jane’s Defense Weekly)

For updates, news, and commentary, follow us on Twitter.

In 2016, the European Union co-funded 17 new robotics projects from the Horizon 2020 Framework Programme for research and innovation. 16 of these resulted from the robotics work programme, and 1 project resulted from the Societal Challenges part of Horizon 2020. The robotics work programme implements the robotics strategy developed by SPARC, the Public-Private Partnership for Robotics in Europe (see the Strategic Research Agenda). 

Every week, euRobotics will publish a video interview with a project, so that you can find out more about their activities. This week features HEPHAESTUS: Highly automatEd PHysical Achievements and performancES using cable roboTs Unique SysHighly automatEd PHysical Achievements and performancES using cable roboTs Unique Systemstems.

Objectives

Hephaestus project addresses novel concepts to introduce Robotics and Autonomous Systems use in the Construction Sector where the presence of this type of products is minor or almost non-existent. It focuses to give novel solutions to one of the most important parts of the construction sector, the part related to the facades and the works that need to be done when this part of a building is built or need maintenance. It proposes a new automatized way to install these products providing a whole solution not only highly industrialized in production but also in installation and maintenance.

Expected impact

Hephaestus aims at automating the On-site Execution or Installation process for empowering and strengthening the Construction Sector in Europe and for positioning the European Robotic Industry as leader and reference in the huge and new growing market for the robotics. Hephaestus solution will allow reducing up to 90% the number of work accidents during façade installation process, reducing around 20% of installation cost and around 44% of the annual maintenance and cleaning costs. Curtain wall construction currently accounts for an annual market of €30,000 million in Europe.

Partners

FUNDACIÓN TECNALIA R&I 
TECHNISCHE UNIVERSITÄT MÜNCHEN
FRAUNHOFER- IPA
CNRS-LIRMM 
CEMVISA VICINAY
NLINK AS 

Coordinator:

Coordinator: Julen Astudillo Larraz, TECNALIA
Julen.astudillo@tecnalia.com

Project website: www.hephaestus-project.eu

Watch all EU-projects videos

If you enjoyed reading this article, you may also want to read:

• New Horizon 2020 robotics projects, 2016: Co4Robots
• New Horizon 2020 robotics projects, 2016: An.Dy
• New Horizon 2020 robotics projects, 2016: BADGER
• Two Horizon 2020 projects researching EU Digital Industrial Platform for Robotics
• EU’s Horizon 2020 has funded $179 million in robotics PPPs

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Emily – short for Emergency Integrated Lifesaving Lanyard – is a remote-controlled rescue boat used by lifeguards to save people’s life at sea (Photo: Hydrolanix – EMILY robot)

This article was first published on the IEC e-tech website.

Rapid advances in technology are revolutionizing the roles of aerial, terrestrial and maritime robotic systems in disaster relief, search and rescue (SAR) and salvage operations. Robots and drones can be deployed quickly in areas deemed too unsafe for humans and are used to guide rescuers, collect data, deliver essential supplies or provide communication services.

Well-established use

The first reported use of SAR robots was to explore the wreckage beneath the collapsed twin towers of the World Trade Center in New York after the September 2001 terrorist attacks. Drones and robots have been used to survey damage after disasters such as the Fukushima Daiichi nuclear power plant accident in Japan in 2011 and the earthquakes in Haiti (2010) and Nepal (2015). Up to now, more than 50 deployments of disaster robots have been documented throughout the world, according to the Texas-based Center for Robot‑Assisted Search & Rescue (CRASAR).

Robin Murphy, head of CRASAR and author of the book Disaster Robotics, says:

The impact of earthquakes, hurricanes, flooding […] is increasing, so the need for robots for all phases of a disaster, from prevention to response and recovery, will increase as well.

Eyes in the sky

Drones, also known as unmanned aerial vehicles (UAVs), can be used to detect and enter damaged buildings, assisting rescue robots and responders on the ground by speeding up the search for survivors through prioritizing which areas to search first. The more quickly SAR teams respond, the higher the survival rate is likely to be. Rescue drones create real-time maps by taking aerial surveys and send back photos, videos and sensor data to support damage assessments.

Drones used for SAR and disaster relief are most commonly powered by rechargeable batteries and are operated autonomously through onboard computers or by remote control. Their equipment typically comprises radar and laser scanners, multiple sensors and video and optical cameras as well as infrared cameras that are used to identify heat signatures of human bodies and other objects. This helps rescuers to locate survivors at night and in large, open environments and to identify hot spots from fires. Listening devices can pick up hard-to-hear audio, while Wi-Fi antennas and other attachments detect signals given off by mobile phones and plot a map that outlines the locations of victims.

New technologies in use or development for rescue drones and robots include ways of increasing survivor detection. Sensors scan areas for heartbeats and breathing, multisensor probes respond to odours or sounds and chemical sensors signal the presence of gases.

Standards put safety first

Much of the technology used in drones comes from commodity electronics developed for consumer essentials like mobile phones. Drones also require global positioning satellite (GPS) units, wireless transmitters, signal processors and microelectromechanical systems (MEMS). The flight controller also collects data from barometric pressure and airspeed sensors.

IEC International Standards produced by a range of IEC Technical Committees (TCs) and Subcommittees (SCs) cover the components of drones such as batteries, MEMS and other sensors, with an emphasis on safety and interoperability.

IEC TC 47: Semiconductor devices, and its SC 47F: Micro electromechanical systems, are responsible for compiling a wide range of International Standards for the semiconductor devices used in sensors and the MEMS essential to the safe operation of drone flights. These include accelerometers, altimeters, magnetometers (compasses), gyroscopes and pressure sensors. IEC TC 56: Dependability, covers the reliability of electronic components and equipment.

IEC TC 2: Rotating machinery, prepares International Standards covering specifications for rotating electrical machines, while IEC TC 91: Electronics assembly technology, is responsible for standards on electronic assembly technologies including components.

IEC SC 21A: Secondary cells and batteries containing alkaline or other non-acid electrolytes, compiles International Standards for batteries used in mobile applications, as well as for large-capacity lithium cells and batteries.

Ideal for isolated and remote hard-to-access areas

Using drones is useful not only when natural disasters make access by air, land, sea or road difficult, but also in isolated regions that lack accessible infrastructure. Recently, drones have started delivering medical supplies in areas where finding emergency healthcare is extremely difficult. In 2014, Médecins Sans Frontières piloted the use of drones to deliver vaccines and medicine in Papua New Guinea. In 2016, the US robotics company Ziplinelaunched a drone delivery service, in partnership with the government of Rwanda, to supply blood and medical supplies throughout the mountainous East African country. Zipline says its battery-powered drones can fly 120 km on a single charge to deliver medicine speedily, without the need for refrigeration or insulation.

Rwanda launches world’s first drone service to deliver blood to patients in remote areas of the country (Photo: Zipline)

A project by a company in the Netherlands to help refugees who get into difficulty in the Mediterranean Sea offers another example of drones being used for humanitarian purposes. Its search and rescue (SAR) drone is intended to fly over long distances, detect boats and drop life jackets, life buoys, food and medicine if necessary.

Currently only about a quarter of the world’s countries regulate the use of drones. Their deployment in disaster relief operations poses challenges involving regulatory issues, particularly when decisions are made on an ad hoc basis by local and national authorities. Humanitarian relief agencies also warn of the risks of relief drones being mistaken for military aircraft.

Two arms good, four arms better

Japan and the US lead the world in the development of rescue and disaster relief robots. Teams from both countries collaborated in recovery efforts after an earthquake and tsunami hit Japan in March 2011, causing a meltdown at the Fukushima nuclear power plant. While a Japanese team operated an eight-meter long snake-like robot fitted with a camera, the US contribution included two remote-controlled robots. The first was a lightweight 22 kg model previously used for bomb disposal and other military tasks before being reconfigured for disaster relief operations. The larger US model, capable of lifting up to 90 kg, was adapted from a device originally used for firefighting and clearing rubble.

Endeavor Robotics 510 Packbots are deployed in emergency situations when direct human intervention is dangerous or impossible, such as after industrial or nuclear accidents. These robots were used at the Fukushima nuclear power plant (Photo: Endeavor Robotics)

In 2017, Japanese researchers unveiled a prototype drone-robot combination for use in disaster relief work. It consisted of a vision-guided robot equipped with sensitive measuring systems, including force sensors, and a drone tethered to the robot. Four fish‑eye cameras mounted on the drone capture video of overhead views, allowing the robot’s operator to assess damage in the surrounding area.

Another Japanese rescue robot unveiled in 2017 is a multi-limbed robot that is 1,7 m tall, with four arms capable of independent operation and four caterpillar treads for mobility. Called The Octopus, it is capable of lifting 200 kg with each arm, crossing uneven terrain and lifting itself over obstacles with two arms while the other two clear debris.

Octopus, developed by Japan’s Waseda University a robot designed to clear rubble in disaster areas including nuclear facilities (Photo: Waseda University)

In the US, researchers are exploring ways in which a small and light collapsible, origami‑inspired robot, first produced by the National Aeronautics and Space Administration (NASA), could be adapted for use as a rescue robot. The device, known as PUFFER (Pop‑Up Flat Folding Explorer Robot) is designed to pack nearly flat for transport, and then re‑expand on site to explore tight nooks and crannies which are inaccessible to larger robots.

Over and underwater too

Given that 80% of the world’s population lives near water, maritime robotic vehicles can also play an important role in disaster relief by inspecting critical underwater infrastructure, mapping damage and identifying sources of pollution to harbours and fishing areas. Maritime robots helped to reopen ports and shipping channels in both Japan and Haiti after major earthquakes in 2011 and 2010 respectively.

In the Mediterranean, a battery‑powered robotic device first developed for use by lifeguards to rescue swimmers has been adapted to help rescue refugees crossing the Aegean Sea from Turkey. This maritime robot has a maximum cruising speed of 35 km/h and can function as a flotation device for 4 people.

Easy to find components and technology
Rescue robots use components and technology found in most other robots used for commercial purposes. Actuators and other electric motors, accelerometers, gyroscopes and dozens of sensors and cameras providing 360⁰ views enable these robots to maintain balance while moving over uneven ground covered with rubble or debris, and to get a sense of the environment around them.

A robot operating in a hazardous environment needs independent power and sensors for specific environments. It may be cut off from its human operator when communication signals are patchy. When remote operation guided by sensor data becomes impossible, a rescue robot needs the ability to make decisions on its own, using machine learning or other artificial intelligence (AI) algorithms.

Several IEC TCs and SCs cooperate on the development of International Standards for the broad range of electrotechnical systems, equipment and applications used in rescue robots. In addition to IEC TC 47: Semiconductor devices and IEC SC 47F: Microelectromechanical systems, mentioned above, other IEC TCs involved in standardization work for specific areas affecting rescue and disaster relief robots include IEC TC 44: Safety of machinery – Electrotechnical aspects; IEC TC 2: Rotating machinery; IEC TC 17: Switchgear and controlgear; and IEC TC 22: Power electronic systems and equipment.

Where humans fear to tread
The number of disasters recorded globally has doubled since the 1980s, with damage and losses estimated at an average of USD 100 billion a year since the start of the new millennium, according to the Overseas Development Institute (ODI), a UK think tank. This trend is likely to lead to increased demand for unmanned robotic devices that can assist disaster relief operations on land, in the air and at sea.

Robots of all kinds play a growing role in supporting SAR teams. Increasing autonomy will create more capable ground robots, while a combination of rapid technological advances and regulation should see the market for disaster relief drones soar over the next five years.

MarketsandMarkets, a research company, estimated in October 2016 that the total global market for drones, comprising commercial and military sales, would grow at a compound annual growth rate (CAGR) of nearly 20% between 2016 and 2022 to exceed USD 21 billion. Drones designed for humanitarian and disaster relief operations will account for 10% of the future drone market, according to the US‑based Association of Unmanned Vehicle Systems International (AUVSI).

The Association for Advancing Automation (A3) cites that between 2010 and 2016, 136,748 robots were shipped to the US —the most in any seven-year period in the US robotics industry. At the same time, US manufacturing employment increased by 894,000 and the unemployment rate fell from 9.8% to 4.7%.

Yet manufacturers, robotics associations, ethicists and media pundits are still fighting the robotics and jobs issue. Brett Brune, Editor in Chief of Smart Manufacturing magazine, argues that “the hand-wringing around robotics and jobs in the US really needs to stop.” 

Manufacturers around the world, including in China, are busy figuring out how quickly to acquire robots. In Mexico, automation is thriving. So much so that the country is now the sixth-biggest auto producer globally. At the same time, the number of manufacturing workers is starting to swell.

A record-high 5.15 million Mexicans worked in manufacturing as of May, nearly a quarter of all workers registered with the country’s social security institute. Around 202,000 Mexicans joined the ranks of manufacturing workers during the first five months of this year alone.

California, which abuts Mexico, has America's largest Hispanic population (14.4 million in 2011), and has had a continuous supply of migrant farm workers since before statehood, has been an agricultural mainstay in the US for close to 100 years and currently produces about 60% of the nation's fresh produce. But as the state's minimum wage approaches $15 and competition from the growing Mexican manufacturing economy mounts, farm managers are having to cope with a workforce that has dropped and immigration policies that threaten to depress the labor supply further.

Since the late 1990s, the number of agricultural workers who move around the US working seasonal farming jobs fell by 60%. According to a study done by the Institute for Research on Labor and Employment (UC Berkeley), half of that labor transformation appears to be due to changes in the demographic makeup of the workforce while government and institutional changes in the market impacted the remaining 50%.

“This reduction in the number of migrant farmworkers increases the risk that fruits and vegetables will not be harvested before they spoil. To avoid this problem, farmers will switch crops, automate planting and harvesting, or take other actions to reduce the need for seasonal agricultural workers. Only a major change in our immigration and guest worker policies is likely to increase migration within the country and postpone automation.”

Interestingly, the trend line for documented workers, although going steadily down, outnumbers undocumented workers as governmental and economic changes in the US and Mexico make immigration less attractive plus demographic changes make farm workers less willing to migrate.

The case for robotics in the ag industry has never been stronger as agricultural migration rates within the United States plummet. Recent research reports suggest that growth in ag technology will be exponential but not necessarily in across-the-board robotics; rather, it will first attack three areas: (1) converting farms to become connected digital systems through the use of precision ag methods, (2) then, with sensor-laden ground-based and airborne drones, planes and satellites gathering and accumulating data, analyzing that data to produce actionable prescriptions, and (3) provide equipment that can vary its procedures (seeding, thinning, weeding, spraying, etc.) based on the actionable prescriptions from #2, and automate post-harvest processing (sorting, inspecting, handling, packaging, boxing, etc.). 

Resources and references

• Story idea from Robotiq weekly What's New blog
• Article: Where did all the migrant farm workers go?
• Article: With farm labor getting scarcer, big U.S. farms are preparing to turn to robots
• Article: What’s slowing the use of robots in the ag industry?
• Research report: Global agricultural robots market, May 2017, 70 pages, TechNavio
• Research report: Agriculture robots market, June 2017, TMR Research
• Research report: Agricultural Robots, December 2016, 225 pages, Tractica

Read more

I’ve written a few times that perhaps the biggest unsolved problem in robocars is how to know we have made them safe enough. While most people think of that in terms of government certification, the truth is that the teams building the cars are very focused on this, and know more about it than any regulator, but they still don’t know enough. The challenge is going to be convincing your board of directors that the car is safe enough to release, for if it is not, it could ruin the company that releases it, at least if it’s a big company with a reputation.

We don’t even have a good definition of what “safe enough” is though most people are roughly taking that as “a safety record superior to the average human.” Some think it should be much more, few think it should be less. Tesla, now with the backing of the NTSB, has noted that their autopilot system — combined with a mix of mostly attentive but some inattentive humans, may have a record superior to the average human, for example, even though with the inattentive humans it is worse.

Last week I attended a conference in Stuttgart devoted to robocar safety testing, part of a larger auto show including an auto testing show. It was interesting to see the main auto testing show — scores of expensive and specialized machines and tools that subject cars to wear and tear, slamming doors thousands of times, baking the surfaces, rattling and vibrating everything. And testing the electronics, too.

In Europe, the focus of testing is very strongly on making sure you are compliant with standards and regulations. That’s true in the USA but not quite as much. It was in Europe some time ago that I learned the word “homologation” which names this process.

There is a lot to be learned from the previous regimes of testing. They have built a lot of tools and learned techniques. But robocars are different beasts, and will fail in different ways. They will definitely not fail the way human drivers do, where usually small things are always going wrong, and an accident happens when 2 or 3 things go wrong at once. The conference included a lot of people working on simulation, which I have been promoting for many years. The one good thing in the NHTSA regulations — the open public database of all incidents — may vanish in the new rules, and it would have made for a great simulator. The companies making the simulators (and the academic world) would have put every incident into a shared simulator so every new car could test itself in every known problem situation.

Still, we will see lots of simulators full of scenarios, and also ways to parameterize them. That means that instead of just testing how a car behaves if somebody cuts it off, you test what it does if it gets cut off with a gap of 1cm, or 10cm, or 1m, or 2m, and by different types of vehicles, and by two at once etc. etc. etc. The nice thing about computers is you can test just about every variation you can think of, and test it in every road situation and every type of weather, at least if your simulator is good enough,

Yoav Hollander, who I met when he came as a student to the program at Singularity U, wrote a report on the approaches to testing he saw at the conference that contains useful insights, particularly on this question of new and old thinking, and what regulations drive vs. liability and fear of the public. He puts it well — traditional and certification oriented testing has a focus on assuring you don’t have “expected bugs” but is poor at finding unexpected ones. Other testing is about finding unexpected bugs. Expected bugs are of the “we’ve seen this sort of thing before, we want to be sure you don’t suffer from it” kind. Unexpected bugs are “something goes wrong that we didn’t know to look for.”

Avoiding old thinking

I believe that we are far from done on the robocar safety question. I think there are startups who have not yet been founded who, in the future, will come up with new techniques both for promoting safety and testing it that nobody has yet thought of. As such, I strongly advise against thinking that we know very much about how to do it yet.

A classic example of things going wrong is the movement towards “explainable AI.” Here, people are concerned that we don’t really know how “black box” neural network tools make the decisions they do. Car regulations in Europe are moving towards banning software that can’t be explained in cars. In the USA, the draft NHTSA regulations also suggest the same thing, though not as strongly.

We may find ourselves in a situation where we take to systems for robocars, one explainable and the other not. We put them through the best testing we can, both in simulator and most importantly in the real world. We find the explainable system has a “safety incident” every 100,000 miles, and the unexplainable system has an incident every 150,000 miles. To me it seems obvious that it would be insane to make a law that demands the former system which, when deployed, will hurt more people. We’ll know why it hurt them. We might be better at fixing the problems, but we also might not — with the unexplainable system we’ll be able to make sure that particular error does not happen again, but we won’t be sure that others very close it it are eliminated.

Testing in sim is a challenge here. In theory, every car should get no errors in sim, because any error found in sim will be fixed or judged as not really an error or so rare as to be unworthy of fixing. Even trained machine learning systems will be retrained until they get no errors in sim. The only way to do this sort of testing in sim will be to have teams generate brand new scenarios in sim that the cars have never seen, and see how they do. We will do this, but it’s hard. Particularly because as the sims get better, there will be fewer and fewer real world situations they don’t contain. At best, the test suite will offer some new highly unusual situations, which may not be the best way to really judge the quality of the cars. In addition, teams will be willing to pay simulator companies well for new and dangerous scenarios in sim for their testing — more than the government agencies will pay for such scenarios. And of course, once a new scenario displays a problem, every customer will fix it and it will become much less valuable. Eventually, as government regulations become more prevalent, homologation companies will charge to test your compliance rate on their test suites, but again, they will need to generate a new suite every time since everybody will want the data to fix any failure. This is not like emissions testing, where they tell you that you went over the emissions limit, and it’s worth testing the same thing again.

The testing was interesting, but my other main focus was on the connected car and security sessions. More on that to come.

In this episode, Audrow Nash interviews Sergey Levine, assistant professor at UC Berkeley, about deep learning on robotics. Levine explains what deep learning is and he discusses the challenges of using deep learning in robotics. Lastly, Levine speaks about his collaboration with Google and some of the surprising behavior that emerged from his deep learning approach (how the system grasps soft objects).

In addition to the main interview, Audrow interviewed Levine about his professional path. They spoke about what questions motivate him, why his PhD experience was different to what he had expected, the value of self-directed learning,  work-life balance, and what he wishes he’d known in graduate school.

A video of Levine’s work in collaboration with Google.

https://www.youtube.com/watch?v=cXaic_k80uM&feature=youtu.be

Sergey Levine

Sergey Levine is an assistant professor at UC Berkeley. His research focuses on robotics and machine learning. In his PhD thesis, he developed a novel guided policy search algorithm for learning complex neural network control policies, which was later applied to enable a range of robotic tasks, including end-to-end training of policies for perception and control. He has also developed algorithms for learning from demonstration, inverse reinforcement learning, efficient training of stochastic neural networks, computer vision, and data-driven character animation.

Links

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• Sergey Levine’s website
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Photo credit: Pierre-Yves Guernier

Technology-driven automation plays a critical role in the global economy, and its visibility in our lives is growing. As technology impacts more and more jobs, individuals and enterprises find themselves wondering what effect the current wave of automation will have on their future economic prospects.

Credit: sk.ru

In this episode, Audrow Nash and Christina Brester conduct interviews at the 2016 International Association of Science Parks and Areas of Innovation conference in Moscow, Russia. They speak with Vadim Kotenev of Rehabot and Motorica about prosthetic hands and rehabilatative devices; and Vagan Martirosyan, CEO of TryFit, a company that uses robotic sensors to help people find shoes that fit them well.

An image of one of the rehabilitative devices from Rehabot.

The robotic platform that scans your feet from TryFit.

Links

• Download mp3 (13.3 MB)
• IASP 2016 conference website
• Skolkovo’s robotics page
• Rehabot’s website
• TryFit’s website
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In this episode, Abate De Mey speaks with Rick Faulk, CEO of Locus Robotics, about warehouse automation with collaborative robots. At Locus Robotics, they increase the productivity of workers in e-commerce warehouses by using robot helpers to transport items that are passed to them by the workers. The lightweight autonomous robots move at a similar pace to their co-workers, use LIDAR and computer vision to detect people and avoid collisions. This allows people to share the warehouse floor with the robots. The collaborative robotic system is lightweight and can be adapted to existing warehouses with minimal alterations.

Rick Faulk
Rick Faulk, CEO at Locus Robotics, leads the executive team and is responsible for overall strategy and execution. He has over thirty years of experience in executive management, sales and marketing for some of the world’s most successful technology companies such as Intronis, j2 Global, WebEx, Intranets.com, Lotus Development, Mzinga, and PictureTel. Rick also currently sits on various boards including INfluitive and Hostway, and is an advisor to a number of early-stage companies.

Links

• Locus Robotics
• Locus Robotics at PROMAT 2017
• Download mp3 (11.9 MB)
• Subscribe to Robots using iTunes
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MIT spinout New Valence Robotics (NVBOTS) has brought to market the only fully automated commercial 3-D printer that’s equipped with cloud-based queuing and automatic part removal, making print jobs quicker and easier for multiple users, and dropping the cost per part.

If you haven’t used a 3-D printer yet, you may be surprised to learn that it isn’t fully automated the way your office’s inkjet is.

With paper printers, users queue documents from a computer, and each finished sheet drops neatly into a tray, waiting to be collected. With commercial 3-D printers, however, designs are manually programmed into the printer, and each finished part is manually removed before starting a new print, which is very time-consuming. At schools and businesses, a trained expert usually handles all prints, which can be expensive.

Now MIT spinout New Valence Robotics (NVBOTS) has brought to market the only fully automated commercial 3-D printer that’s equipped with cloud-based queuing and automatic part removal, making print jobs quicker and easier for multiple users, and dropping the cost per part.

To use the printer, called NVPro, a user submits a project from any device, which queues up in the NVCloud software. When a part gets printed, a retractable blade cuts the piece out and moves it into a bin, and the next project begins automatically. Projects can be monitored remotely via webcam.

Invented several years ago in an MIT fraternity house’s basement and commercially launched last April, the printer is now used at more than 100 businesses and schools, primarily as an education tool. Over the past year, there have been more than 84,000 prints, saving more than 165,000 labor hours, according to the startup.

NVLabs, the startup’s research arm, is now bolstering security, analyzing big data, working with materials, and improving human-robotic interfaces for 3-D printing. In January, NVLabs spun out Digital Alloys, a startup developing high-speed, multimetal manufacturing systems that run at lower costs than traditional systems.

The company envisions a world in which 3-D printing is as easy and commonplace as printing on paper — and perhaps more globally accessible, says chair and co-founder Alfonso (A.J.) Perez ’13, MEng ’14. “Our mission is to allow printing with any material, at any time, and from anywhere in the world. This means you could be on a beach in Bora Bora and control a machine at MIT with the click of one button,” he says.

The other co-founders are Chris Haid ’14, general manager at NVBOTS; Forrest Pieper ’14, director of software at Digital Alloys; and Mateo Peña Doll, director of hardware engineering at Digital Alloys.

From the basement to NVBOTS

As undergraduates, the four NVBOTS co-founders were brothers of Phi Delta Theta and dedicated “makers.” Outside the classroom, they worked on various projects that involved 3-D printing parts, such as medical devices and a number of other gadgets. But whenever they wanted a part, they’d submit a computer-aided design (CAD) file to a 3-D printer location on campus and wait for the product, which took days.

To expedite production, the group built a custom 3-D printer in the fraternity house’s basement. It was exciting at first, but everyone in the house soon wanted to print at the same time. The solution was a cloud-based queue and webcam to monitor the printer which, at the time, was the first of its kind, Perez says.

But each part still needed to be manually removed. “If it was 3:00 in the morning, and you wanted to start the next job, you had to get up and go down into the basement and start it,” Perez says. “That was an untenable solution for us lazy students.”

To rectify that issue, the students created an automated part-ejection mechanism. After a part has been printed, the print plate is lowered. A blade moves over to the plate, wedging underneath the part. As it moves across the plate, the blade cuts away the part and pushes it into a bin. The blade retracts and the plate rises back up.

Being new to the technology, Perez was shocked that, despite decades of operation, 3-D printers couldn’t automatically eject parts. The four students filed patents with the MIT Technology Licensing Office but didn’t plan on becoming entrepreneurs. That changed after they brought to the 2013 MIT Tech Fair “a quintessential MIT prototype — a rat’s nest of wires,” Perez says.

Numerous 3-D-printing experts asked the students where to buy the automatic part-remover. Moreover, novices didn’t realize the technology and the queuing software were new. “When the early adopters — the experts — are saying that this is the solution to a problem, and the general population already expects the solution to exist, that was the ‘ah-ha’ moment,” Perez says.

That summer, “things got serious,” Perez says, when the co-founders entered the Global Founders Skills Accelerator (now MIT delta v), held in the Martin Trust Center for MIT Entrepreneurship. There, they fleshed out a business plan and learned about profit margins, earning customers, and company ownership, and they officially incorporated NVBOTS.

For 6-year-olds and PhDs

NVBOTS initially designed printers for education. The idea is to develop curricula that uses 3-D-printed models as teaching aids for any subject — such as printing Roman figures for history lessons, Egyptian art, or a human heart for an anatomy class.

“If you want to teach the physics of projectile motion, you can use our catapult lesson that teaches about the math by flinging balls across the room,” Perez says. That type of hands-on learning, he adds, helps “tickle those emotions that MIT shoots for — ‘mens et manus,’ mind and hand.”

Pilot programs began in 2014 at Newton North High School and the Sizer School in Fitchburg; the printers are now in dozens of schools in the region and beyond. Printers are usually put in a central area for all to use. Every student can sign up for an account to submit parts for printing, but only one administrator approves or rejects the parts. MIT Copytech is currently piloting a printing service with the NVPro.

The machines require minimal training and oversight, which is a benefit for schools, Perez says. “Teachers like to teach. Teachers don’t like to maintain a machine, and schools can’t afford to pay for someone to run a machine,” he says. “On a basic level, we wanted to create a 3-D printer that’s so easy a 6-year-old could use it, but a PhD could use it as well.”

In fact, Perez notes, the printer only has one button: “the giant emergency stop button.”

Over the past year, the startup has also sold to companies in automotive, construction, medical devices, consumer goods, aerospace, and architecture fields. Usually it takes days or weeks to 3-D print, say, a building model or a medical device part. With NVBOTS, clients can upload a design on the cloud and pick up the product themselves within a day, with no need of a trained expert. The reduced cost of ownership and the labor saved significantly lowers the price per part compared to traditional 3-D printers, Perez says.

This also gives more workers the ability to find solutions for engineering and other company issues, Perez says: “Everyone who signs up for an account can become a problem solver.”

Making metals, improving printers

Moving forward, NVLabs and Digital Alloys will bring even more innovations to 3-D printing, Perez says.

Digital Alloys is developing a new print head that, according to the startup, allows for multimetal prints at significantly higher speeds than traditional methods, for aerospace, defense, and automotive applications.

Traditionally, metal printing systems are a layer-by-layer slog. One layer of powder, about 20 to 50 microns thick, gets spread across a plate. A laser positioned above the plate melts one layer of powder to the layer below. This repeats until the object is complete. Digital Alloys’ process uses thicker wire as the material, which builds up more quickly. The industry leader for metal printing produces 2 kilograms per day, while Digital Alloys produces 20 kilograms, Perez says.

NVLabs is gathering data on NVBOTS printers to improve performance. Each time a user prints, the startup gathers data on the job’s success, quality, length, and material, among other information. Since launch, the startup has generated 3.8 terabytes of data related to the manufacturing process. The lab is also tackling security issues, improving human-machine interfaces, and, as a major challenge, enabling 3-D printing with any plastic.

“There’s a crazy world of polymers that can be used for printing,” Perez says. “That’s a big aspect of what we’re working on.”

Fastbrick’s Hadrian X prototype.

RobotLabNYC’s third installment will be on June 13, in New York City with Howard Morgan (FirstRound Capital) and Tom Ryden (MassRobotics); together, we will be “Exploring The Autonomous Future” (RSVP today). Coincidentally, Jimmy Fallon featured a new bit this week called “Showbotics,” providing viewers with a sneak peek into the robotic future:

While Fallon pokes fun, the reality is that robots today are showing up for work in record numbers. As America pulls out of NAFTA and starts a trade war with Canada over lumber imports, it is predicted that home building costs could increase by more than 20% over the next year. In order to keep America building without sacrificing margin, labor is shifting from humans with tool belts to job-ready robots.

An example of machines being added to the field is MIT’s Digital Construction Platform (DCP) – a 3D-printing fabrication robot. The DCP consists of a robotic arm that can print structures made of insulating foam at architectural scale — in other words, big enough to live in. In a test last July, MIT researchers printed an open dome structure in two days that was as large as a ranch house. The average single-family home takes about 7 months to build (US Census Bureau).

According to the white paper published this week in Science Robotics, the researchers claim, “this printed test structure is currently both the largest monolithic structure ever 3D-printed by an on-site mobile platform and the fastest autonomously printed architectural-scale structure.”

Shanghai-based WinSun 3D-printed walls last year for a six-story apartment building out of concrete that was assembled on site for immediate occupancy. According to WinSun, they were able “to save 60% of the materials typically needed to construct a home and can be printed in a time span which equates to just 30% of that of traditional construction. In total, 80% less labor is needed, meaning more affordable construction, and less risk of injury to contractors.”

In the USA, Blueprint Robotics is manufacturing wood-framed houses today with industrial robots. Builders upload their architectural plans to Blueprint’s cloud-based platform which outputs within weeks pre-made walls, floors, and roofs. While the concept of pre-fabricated houses is not novel -Sears Roebuck & Co first introduced kits to homesteaders in the 1900s – Blueprint is the first to create an automated production line that pre-places everything from sheetrock to nails to lighting placement. This means modular structures can now be used for bigger and more luxurious developments from multi-million dollar mansions to large apartment buildings. Bloomberg News reported this week that even Marriott, the world’s largest hotel operator, is “turning to modular construction for some of its properties,” shipping the walls inside the same containers as the television sets and beds.

According to leading home consultant, John Burns, “this has to be the wave of the future — I don’t know how we solve the labor shortage otherwise. What drives modular construction is the ability to build the house more cost-effectively.” The Bureau of Labor states that almost 200,000 construction jobs are unfilled across the country at a boom time for homebuilders. The U.S. construction industry’s unemployment is at its lowest level in a decade as fewer young people enter the construction workforce. The door remains wide open for robotic disruption.

Jerry Smalley, CEO of Blueprint, claims his mechanized platform is filling this labor gap. While manual labor is greatly reduced, skilled tradesmen are still high demand, according to Smalley, “robots cut the hole, but somebody still has to put the electrical box and pipes in the right places.” Blueprint utilizes Germany-based Weinmann’s Stiles machines that have been shipping pre-fabricated homes in Europe for decades.

Construction globally generates over $8 trillion annually. It is also one of the most labor-intensive professions with concrete casting, wood framing and bricklaying. As a result, the construction industry accounts for almost 20% of US workplace fatalities. Masons are particularly prone to musculoskeletal injuries, due to repetitive motion strain from carrying and laying over 500 bricks a day by hand.

New York-based Construction Robotics is revolutionizing the masonry trade with its SAM100 (Semi-Automated Mason) robot, capable of working six times faster than the average human. SAM uses a combination of a conveyor belt, robotic arm, and concrete pump. Construction Robotics recently upgraded its product to enable the laying of popular “solider course” bricks. SAM costs the equivalent of the average salary of 10 masons or $500,000 and is already being deployed on sites throughout America.

Construction Robotics’s head of business development, Zachary Podkaminer said, “we don’t see construction sites being fully automated for decades, if not centuries. This is about collaboration between human workers and machines. What SAM does is to pick up the bricks, put mortar on them, and puts it on the wall. It still requires a mason to work alongside it. SAM’s just there to do the heavy lifting.”

Already Construction Robotics has competition from Australian-based Fastbrick Robotics. Fastbrick’s Hadrian X prototype claims to place bricks at an impressive rate of 1,000 per hour. The robot (shown above) can handle different brick sizes as well as cut, grind, mill, and route the bricks to fit any structure before putting them in place. Unlike SAM, which uses traditional bricks and mortar, the Hadrian machine is optimized for interlocking precision bricks (that are 15 times larger) and construction adhesive to hold the concrete structure.

Fastbrick Operation Manager, Gary Paull, says his system “will produce structurally sound wall to renderable quality at speeds never seen before.” The company plans to market the Hadrian bricklayer first in Western Australia and then to the rest of the country before offering it worldwide.

Deployment of task-orientated robots will be advanced further by the coming inevitability of autonomous vehicles ferrying heavy construction equipment and materials on the site.  According to Jenny Elfsberg of emerging technologies at Volvo CE, “Autonomous machines will increase safety in hazardous working environments and eliminate the possibility of accidents caused by human error. They will also perform repetitive tasks more efficiently and precisely than a human operator. And because machines will be operated in the most efficient way, customers will benefit from improved performance, productivity, fuel efficiency and durability.”

In a one-hour comparison, it was found that the autonomous wheel loader could reach the equivalent of 70% of that of a skilled operator’s productivity levels when loading and unloading. The machine has also done ‘real work’ for a Volvo CE customer at an asphalt plant in Sweden.

“In the future, you could also potentially have one operator for three or four machines, increasing productivity and further decreasing costs,” Elfsberg states. “Looking ahead, I imagine that autonomous machines will be smaller and more robust. There will be no need for a cab or suspension – much like the HX1 concept [shown above],” which Volvo CE unveiled last year.

Mining company Rio Tinto is deploying a fleet of close to 100 driverless dump trucks (manufactured by Komatsu) to move high-grade ore autonomously. It is also developing and testing an autonomous heavy-haul long-distance railway system, and has deployed an automated blast-hole drill system that allows a single operator to remotely control multiple drill rigs.

According to a Rio Tinto press release, “these driverless vehicles deliver their loads more efficiently, minimizing delays and fuel use, and are controlled remotely by operators who exert more control over their environment and ensure greater operational safety.”

Industry leaders, like Caterpillar and GE, are taking note by funding innovation at early financing rounds that are typical for corporate venture. Last year GE and Caterpillar invested in Clearpath Robotics’ $30 million Series B round to expand its fleet of autonomous mobile robots into industrial settings, like construction and mining. Previously, Clearpath’s mobile robots were primarily utilized in warehouses moving boxes and pallets around distribution centers.

This past March, Caterpillar co-hosted an industrial startup competition at SxSw’s Interactive Conference with their local dealer Holt. Leading autonomous navigation company 5D Robotics walked away with the $20,000 grand prize. 5D is currently working with United Rentals to convert their fleet of manual industrial machines into autonomous mobile robots. While Caterpillar has been working on self-driving trucks internally since the 1990s, reaching outside for innovation could be the difference in dominating the new autonomous landscape.

This new sentiment was reflected in its “Year In Review” investor statement: “For 90 years, Caterpillar has delivered breakthrough innovation inside our machines and engines. Today, that innovation is increasingly happening outside the machine. We’re going ‘beyond the yellow iron,’ harnessing the power of big data to offer our customers insights that decrease operating costs, increase uptime and maximize profitability.”

Heavy Water Components Test Reactor decommissioning.

How can we create robots that can carry out important tasks in dangerous environments? Machine learning is supporting advances in the field of robotics. To find out more, we talked to Dr Rustam Stolkin, Royal Society Industry Fellow for Nuclear Robotics, Professor of Robotics at the University of Birmingham, and Director at A.R.M Robotics Ltd, about his work combining machine learning and robotics to create practical solutions to nuclear problems.

What drew you to engineering and robotics?

There are many definitions of engineering, but the one I like is “the creation of artefacts for the benefit of mankind”. Engineering is a way of using science to be creative and to create novel technologies which can bring major societal and economic benefit.

Dr Rustam Stolkin. Source: University of Birmingham/YouTube

Robotics, in particular, attracted me because robots are inherently inter-disciplinary: they use mechanics and electronics, as well as computational elements, such as machine learning and artificial intelligence. This has allowed me to become an “expert generalist”; I specialise in being extremely cross-disciplinary. At the University of Birmingham, I have research collaborations spanning eight different schools across three colleges on the campus.

Tell us about your research?

My work focuses on developing advanced robotics technologies for nuclear decommissioning, demolishing legacy nuclear sites, and safely packaging, storing and monitoring any radiological or contaminated materials. The UK contains 4.9 million tonnes of legacy nuclear waste, mainly by-products of weapons production during the Cold War, alongside a smaller contribution from nuclear power use for civilian electricity generation. Cleaning up the UK’s legacy waste is the biggest environmental remediation project across Europe; it could take over 100 years and cost up to £220billion.

Factories are highly structured environments, where robots can be pre-programmed to make repetitive motions. This has supported the revolution in robotics that we’ve seen since the 1980s. In contrast, there has been surprisingly little use of robots in the nuclear industry, aside from a very small number of robotic vehicles deployed after the Fukushima disaster, and a very few cases of deploying bespoke robotic manipulators, controlled in rudimentary ways by joystick teleoperation.

This is in part because legacy nuclear sites, some of them over 60 years old, are highly complex and unknown environments, such as contaminated buildings that have been sealed off for decades. They have an enormous diversity of scenes, materials, and objects that nuclear decommissioning robots must interact with in complex ways, such as by grasping and cutting objects.

We have only recently discovered how to use state-of-the-art machine learning methods to help robots learn adaptive and transferable skills which they can apply in new environments. This allows them to perceive and handle new kinds of objects which they have not seen before. For example, using machine learning, a robot can be trained how to grasp and pick up one object, and then use this knowledge to adapt the grasp for a new object with a different shape.

Recent “deep learning” approaches might offer part of the solution in navigating these complex environments. However such learning approaches can rely on large amounts of “training data” – labelled examples of images of objects for example – which can be used to train the robot so that it can recognise similar objects in future. It is still very unclear how to generate sufficient training data in order to enable robots to learn how to behave in useful ways in complex real environments.

Why do we need robots to carry out nuclear decommissioning?

Contaminated materials are currently decommissioned by human workers in air-fed plastic suits, which is not only unsustainable but can be dangerous. Using robots means humans do not need to enter highly hazardous work environments, and means that zones that humans cannot enter (even in protective suits) due to high gamma radiation can still be decommissioned. Using robots also reduces “secondary nuclear waste”; for every one container filled with actual primary nuclear waste, more than ten containers become filled with contaminated plastic suits, respirators, rubber gloves, and other “secondary waste” from human entries.

How have you used machine learning to develop robots capable of nuclear decommissioning?

My lab has used robots to perform fully autonomous grasping and manipulation of nuclear waste-like objects and grasping of moving objects.

We also use state-of-the-art deep-learning neural networks to do real-time 3D reconstruction of a room, and simultaneously recognise and label materials such as concrete, metal, wood and fabric. This helps us develop ways for robots to characterise nuclear environments and nuclear waste objects in real-time. We are extending these methods to also combine visual camera information with other kinds of data from radiation, thermal and other sensors.

Where do you see machine learning going within your field of research?

Robotics engineers often argue that machine learning has only been used on relatively simple problems so far. Achieving machine learning of real robotic actions, with real objects in real scenes, is a significantly more complicated challenge that is only just starting to be attempted.

Innovative new approaches will emerge in the next few years for overcoming these challenges in enabling useful robotic behaviours in real environments. The next few years will see an interesting fusion of conventional approaches, such as hand-crafted robot control algorithms, with new approaches, like machine learning of robot behaviours.

Human-robot interaction, and human-AI collaborative working will also become increasingly important. What we are working towards now is “shared control” in which artificial intelligence and humans collaborate together to control a remote robot. For example, recent research from the French institute CNRS shows human operators controlling the motion of a robot arm and gripper in relation to an object, while the AI automatically orients the gripper with the object. Simultaneously, the AI automatically controls a second robot arm to move a camera so that it always offers the best viewpoint. Thus, human and AI collaborate to control a pair of arms for a remote handling task. In the future, this kind of human-machine collaboration could transform how we interact with nuclear and hazardous environments.

The Royal Society is currently carrying out a major policy project on machine learning, and its impact for the UK economy and society. To find out more please visit our project page.

This post was originally published by the Royal Society. Click here to view the original post.

Delivery robots are touted as gaining widespread popularity in the near future. Wheeled models could be suitable for urban areas, while UAVs have great potential in accessing difficult areas and carrying a variety of payloads. But first we have to overcome technical barriers, safety issues and more than a few legal aspects.

We are use to thinking that a single UAV will only transport a single small box. However a mass delivery service should also focus on the possibility of charging big solids in the pursuit of service viability and success. In this scenario, UAV cooperative teams could play a key role in the industry. This idea has proven to be feasible, as ETH shows:

UAV swarms could be useful in cases where payloads are too heavy to be charged by a single multirotor, its shape is large, or even with deformable solids (such as wires or ropes). Two or more drones can form a swarm.

In the case of deformable linear solids, the payload could be the solid itself (for instance, high tension lines and tethered multirotors), or we could make use of the ropes to transport a payload attached to them. In fact, this type of transportation solution is not only needed for commercial delivery purposes, but also might be useful for cable manipulation in catastrophic indoor scenarios or maintenance operations in narrow spaces. A wire linking some drones restricts their behaviour differently from solid bars links or no links at all.

https://www.youtube.com/watch?v=Cb6DBu8pHO0

Cooperative transportation systems require a specific control and path planning strategy compared to single robots. Deformable solids act as passive loads that affect robot dynamics, and a proper coordination among robots is essential for an efficient energy usage. Imagine a drone running out of battery faster than the rest of the swarm. Equi-load distribution of all the team members, and keeping the payload clamped to the gravity centre of the UAV, are some of the basic assumptions to be made in this kind of systems. An additional requisite is that particularly in paths with obstacles, the displaced distance of each drone also is very important. About engineering control, different options may vary depending on the centralised or decentralised control of the drones, and deterministic type of control (such as PID or LQR) are showing good results.

Deformable solid linking among flying vehicles is not a new phenomenon: aerial refuelling already exists, but with manned vehicles. Unmanned vehicles present new challenges, and researchers have advanced this field in the last decade, although hundreds of real tests under wind conditions and variable payloads are necessary to achieve a fully operative and robust system.

References:

Estevez, J., Graña, M., & Lopez-Guede, J. M. (2016). “Online fuzzy modulated adaptive PD control for cooperative aerial transportation of deformable linear objects”. Integrated Computer-Aided Engineering, 24(1), pp. 41-55.

Maza, I., Kondak, K., Bernard, M., & Ollero, A. (2010). “Multi-UAV cooperation and control for load transportation and deployment”. Journal of Intelligent and Robotic Systems, 57(1-4), 417.

Michael, N., Fink, J., & Kumar, V. (2011). “Cooperative manipulation and transportation with aerial robots”. Autonomous Robots, 30(1), 73-86.

Palunko, R. Fierro, and P. Cruz (2012). “Trajectory generation for swing-free maneuvers of a quadrotor with suspended payload: A dynamic programming approach”. In Robotics and Automation (ICRA), 2012 IEEE International Conference on, pages 2691–2697.