Robots in Depth is a new video series featuring interviews with researchers, entrepreneurs, VC investors, and policy makers in robotics, hosted by Per Sjöborg. In this interview, Andra Keay, director of Silicon Valley Robotics, talks about opportunities and challenges for robotics startups and how developments in the agricultural sector can aid consumer robots.

Andra explains how Silicon Valley Robotics formed and what they do, including organising networking events such as the annual Robot Block Party.

She also shares her views on the areas of robotics where we will see significant developments in the near future, including progress in agricultural robotics that could feed into improvements in other industries, such as home robots.

You can support Robots in Depth on Patreon. Check out all the Robots in Depth videos here.

If you liked this article, you may also be interested in:

• Five women robotics experts from Silicon Valley share their stories
• Money continues to flow into robotics startups
• Coming of age: Clearpath’s Ryan Gariepy on growing a robotics startup
• Meet the winners of #RobotLaunch2015

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Robots in Depth is a new video series featuring interviews with researchers, entrepreneurs, VC investors, and policy makers in robotics, hosted by Per Sjöborg. In this interview, Michael Rubenstein describes how he has taken his robotics research from theory into practice by building cheap and small robots — 1024 of them to be exact.

Michael first talks about his PhD research in algorithms for modular self-reconfigurable robots at the University of Southern California, supervised by Wei-Min Shen.

He then shares some of the challenges involved in building 1,024 robots, and how what you learn from actually building robots is different from what you learn from simulating them. This project helped him fine-tune the algorithms from his earlier research.

By working on Kilobot, Michael also learned how to make inexpensive robots that fit the educational market well, and has created robots that can be programmed by school children at robotics summer camps.

The Kilobot work was done in the Self-Organizing Systems Research Group at Harvard University. Michael is now faculty at Northwestern University.

You can support Robots in Depth on Patreon. Check out all the Robots in Depth videos here.

If you liked this article, you may also be interested in:

• Thousand-robot swarm self-assembles into arbitrary shapes
•  Bioinspired robotics #1: Swarm collectives, with Radhika Nagpal
• Droplets: A low-cost swarm robotics platform for teaching and experimentation
• Robots Podcast #98: Self-organizing systems

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Komissarova describes why the timing for starting Grishin Robotics was just right, and shares some of the initial feedback from different parts of the robotics community.

She also talks about differences between the companies that get funded and the ones that that don’t, what it takes to be a successful entrepreneur in robotics, and the areas within consumer robotics where we are likely to see strong development in the next few years.

You can support Robots in Depth on Patreon. Check out all the Robots in Depth videos here.

If you liked this article, you may also be interested in:

• Robots Podcast#116: Grishin Robotics
• Dmitry Grishin on evaluating robotic investment opportunities
• Grishin’s first investment: Double Robotics

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Foote explains the benefits of open source and how it helps make research and development more efficient when both researchers and start-ups can focus on their added value, rather than starting from scratch. This is an advantage for the whole robotics community, enabling it to move forward much faster. 

Foote also describes how developers can contribute directly to further developing the ROS standard, for the benefit of the robotics community. 

You can support Robots in Depth on Patreon. Check out all the Robots in Depth videos here.

Melonee shares how she first got into building things at a young age and how that led to studying mechanical engineering and leaving her PhD project behind to become the second employee of Willow Garage. She shares some personal anecdotes from the first few years at Willow Garage, including both successes like the PR2 and some less successful moments.

Melonee also gives her perspective on the development phase robotics is in now and what the remaining challenges are. Related to that, she discusses what is feasible to deliver in the next five years vs. what her dream robot would be.

You can support Robots in Depth on Patreon. Check out all the Robots in Depth videos here.

Robots in Depth is recorded at different robotics events around the world. You can support Robots in Depth on Patreon.

Transcript included.

In this episode, Per Sjöborg talks to Hans-Peter Grothaus, from CLAAS, about automation in agriculture.

There is a small window of time when crops are ready to harvest. If, during this window, not all of the crops are harvested, the farmer can experience large financial losses. This means that systems that automate fieldwork must be reliable. And reliability, or robustness, is a major challenge because field conditions change during the day (it could start raining) and it is tough to know soil conditions in advance. In this interview, these challenges are discussed, as well as how field data can help farmers make decisions and the market for agricultural robotics.

Hans-Peter Grothaus

Dr. Hans-Peter Grothaus studied Agriculture Sciences at the Universität Göttingen in Germany, where he wrote his dissertation. Since 2008, Grothaus has been the head of development for system-based services at CLAAS in Harsewinkel, Germany.

Links:

• Download mp3 (15.1 MB)
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS

Transcript

Per Sjöborg:  Welcome to the podcast. I’m here with Hans-Peter Grothaus from CLAAS, and we’re going to talk about automation and fleet integration within the agricultural community, a reasonably well established field in advanced automation. But it’s still growing and adding features as we go along. Could you tell our listeners, who may never have been on a farm, what kind of equipment you’re working with and how it is used by your customers?

Hans-Peter Grothaus:  What we’re using is harvesting machines and also machines for tractors, for overloading the harvested material, for infield processes and also for transportation, to bring the harvested goods to the farm.

Per Sjöborg:  Now we’re talking about corn, potatoes, and what else?

Hans-Peter Grothaus:  So, we work more with grain and grapeseed and all the fruits that are above the earth, nothing under the earth.

Per Sjöborg:  Yeah, because the harvester is in the middle of a network; it can’t do its job on its own; it has to have a fleet of tractors with carrying capacity and then trucks to get to a storage facility, and all of these integrate and work together. Could you tell us a bit more about the processes you use and how these are optimized?

Hans-Peter Grothaus:  So, traditionally we have single harvesting machines, with an overloading facility at the end of the field. But in bigger farms in larger surroundings, we also have multiple harvesting machines working together with overloading facilities, which bring the harvested goods to a truck waiting at the end of the field.

Per Sjöborg: So this means that there are actually two instances of offloading and unloading: from the harvester to the tractor, that is able to operate in the field, and then from the tractor to the truck that is only able  to operate on regular roads.

Hans-Peter Grothaus:  Yes, that’s true, because the trucks are normally not used in the fields, therefore we use tractors and special overloading facilities; overloading wagons which have low tire pressure so that the ground is not damaged by the tires.

Per Sjöborg:  And the vehicles don’t get stuck and so forth.

Hans-Peter Grothaus:  Yes.

Per Sjöborg:  And all this has to be optimized, of course, so that we use as little fuel as possible and so that we can harvest as much as possible in as short a time as possible. I guess that’s very hard to optimize properly.

Hans-Peter Grothaus:  Yes, you have different goals. For example, when you harvest depends on weather conditions and on the available equipment you have, and therefore you have to plan. We are developing infield planning systems. Just imagine you have a navigation system and you can say, “Okay, I want to be ready early with my harvest because bad weather is coming,” or you have very stable weather conditions and you say, “Okay, I have another target, I want to have the highest quality, with low fuel consumption,” or you want to have the same process but not drive over the whole field to avoid soil compaction.

Per Sjöborg:  Yes, because, as you’ve said, you have no roof over your operation and so you really feel the robotics operating out there under rapidly changing conditions. I mean, you can have a prediction of good weather but it can change immediately, or in a very short period of time, and you have to re-optimize everything.

Hans-Peter Grothaus:  Yes, you know that you have to harvest once a year, you know that weeks and years beforehand, but what you don’t know are the actual conditions. And you don’t know exactly what’s grown on a certain plot – that can vary a lot, even within a field. You don’t know the condition of the soil, how well you can drive on it or how weather conditions will develop throughout the day. So, when you start harvesting everything it could be optimal, and after two hours it’s not so optimal, so you have to change the settings of the fleet and the single machines.

Per Sjöborg:  Then, of course, this dynamically redefines itself all the time towards the overall goals of the operation.

Hans-Peter Grothaus:  Yes.

Per Sjöborg:  I understand that you use navigation systems, usually GPS. Do you also use any visual verification of where you are in the field, where other machines are in the field?

Hans-Peter Grothaus:  For steering purposes, we have different solutions on the market. We have camera based solutions and we have GPS based solutions, with correction signals that are accurate to two centimeters. So that works well. We also have laser-based systems that are for steering the machines. All these planning tools are in development. The first prototypes work quite well. The first step is to show the other drivers the actual location of a machine. When you imagine a field of wheat or barley, you can see the machine on the horizon, but when you have a maize field – maize is four meters high – you might only hear the machine, but not know exactly where it is. When you can see that the other machine is at a certain spot and you can see how full the overloading bin already is – this information helps you.

Per Sjöborg:  Especially, as you mentioned, in a maize field, where it might be very scary to see a big combine harvester only meters away!

Hans-Peter Grothaus:  Or you only hear it.

Per Sjöborg:  Yeah, but where is it? Because you’re in this high maize field, in a smaller vehicle.

Hans-Peter Grothaus:  Yes, but that is not the main danger, because you can react. For these inferior processes, you need the right overloading position. With a maize harvest, there is a lot of bulky material which you have to transport. Just for finding the right rendezvous point – where the transportation vehicles should meet, to avoid waiting time and additional driving – it helps a lot and takes the stress out of the process.

Per Sjöborg:  You use a lot of sensors to collect data about what is being harvested in real time. So, you know that, for instance, next year this area of the field can give X amount of return. What kind of sensors are you using to detect the quality of the grain etc.? They have to be very reliable and advanced, if the farmer only has a window of a couple of weeks or a month.

Hans-Peter Grothaus:  We use different cameras. For example, still cameras to detect grain quality, and this was hard to develop because there are difficult surroundings in which we use them and therefore they have to be very robust. They’re very reliable because, if they fail, then it takes a long time to fix. The farmer has to do his harvest right now, not in two weeks time right? Farmers want the machines to work without interruption during the harvesting period.

Per Sjöborg:  It’s a stressful period for the farmer.

Hans-Peter Grothaus:  Yes, so they have to be very reliable during the harvesting period. That could be part of the premium our customers pay for the products, because they’re very reliable. We do everything we can to ensure that the machine does not fail in the harvesting period. So there are lots of sensors and condition monitoring. We can make yield maps, so we can map exactly how much grain or other goods were harvested at a certain plot, and maps where you make overlays over a number of years and you can see how much you have harvested in a certain area. With this information, you can decide how much fertilizer to use on a plot to manage it. So, you see, there is an inter-linkage between the machines, processes over the years and the management systems. You have connected managing systems, with all the data from the whole farm and the machines input data but also use the data from this management system.

Per Sjöborg:  This also goes into the optimization process, of course. Say, if we have dry weather now but we are expecting wet weather, we can optimize to harvest the part of the field where the soil is usually the wettest, because it might be un-drivable if it rains further on in the day or the week. So you can optimize the data for that?

Hans-Peter Grothaus:  Yes, when you know that, you can start harvesting a certain area of a field earlier. You can also use your machine for a longer period, but when you are using a contractor for the first time, where should he get the information from, to know where to start? So, normally he drives around and then, after some time, he knows what to do, but you can save time if you give him additional information.

Per Sjöborg:  And in this reasonably small window of harvesting time, every hour, every half a day counts a lot. It’s very important to do it on time.

Hans-Peter Grothaus:  Yes, that’s true, and systems which help farmers to make better quality decisions are economical because an hour of harvesting time is very expensive, especially if you are not able to use it.

Per Sjöborg:  If weather catches up with you.

Hans-Peter Grothaus:  If the weather catches up and you have no additional capacity, that would be the worst case scenario.

Per Sjöborg:  Harvesting is one of your major businesses but you do other things in agriculture too. We’ve already mentioned pesticides and fertilizing, but there’s also tilling. Can you give us a short overview of your work in non-harvesting related activities?

Hans-Peter Grothaus:  We also have tractors and these act with implements on the farms, so we don’t have to produce sprayers or fertilizers. We produce machines for grass harvesting, for example. There is also a lot of intelligence, such as systems where the tractor is steered by the implement; the implement in these processes is much more intelligent than the tractor.

Per Sjöborg:  Because it knows what it’s doing and what it needs from the tractor to do it the best way.

Hans-Peter Grothaus:  Yes, the implement knows its own process, and the implement has more and more intelligence, and processes and computers. The idea is that the tractor gets an image of the implement and you can steer it, but the intelligence is in the implement.

Per Sjöborg:  Because it’s unique to a particular task. This is very interesting. As I understand it, the process of using automated and assisted driving is more or less standard today, so could you describe how these things have been selling in the farming industry, because I think that many people out there have ideas for robotics and need information on how to sell this to a community. How did you convince farmers that this reasonably advanced level of technology would work, and that it would be beneficial and they could handle it? How did you convince them that this was a good idea?

Hans-Peter Grothaus:  Most professional farmers and most customers can decide very rationally, if they see that a system helps them work for a longer period without exhaustion, and helps them to save money because the steering is more accurate. Just imagine you’re mowing your lawn and you always overlap a little, because you don’t want grass to be left. In the harvesting process, when you have to cut a bar with twelve meters, nobody is able to do it with such great accuracy and at such a distance, so we always overlap, maybe half a meter.

Per Sjöborg:  Which is a lot if you’re doing many fields.

Hans-Peter Grothaus:  Which is a lot if you’re doing kilometers of driving a day.

Per Sjöborg:  So if you can get that down to two decimeters…

Hans-Peter Grothaus:  With ten centimeters, you have gained a lot on fuel consumption. Less time means less soil compaction. So the steering systems pay off in one year now. Other systems? That is when our customers look at all the new features and say, “I don’t know if I need it.” But if they do buy it, they get used to it because the work gets easier; it’s more relaxing, less stressful and the farmer can, for example, do management tasks on the machine. You don’t need to do stupid work, like steering the machine. It does it on its own.

Per Sjöborg:  Yeah, and it does it better, but I guess he is better at the management though.

Hans-Peter Grothaus:  Yes, but he has to supervise the processes, to see if there are obstacles that the machine may not have seen.

Per Sjöborg:  This leads us to the future of these systems. Are we going to see these fleets become fully autonomous? You’ll have someone overseeing a fleet of these vehicles – a few harvesters and the truck distribution network we talked about – to the edge of the field. I don’t think that the actual truck taking it to the storage facility is a problem. Will we see fully automated fields in the near future?

Hans-Peter Grothaus:  I can imagine that we’ll see more “master-slave” based systems in the near future. The labor cost is not the limiting factor at the moment, because the machines are not very cheap but, on the other hand, you have to bring all the machines into the field and you have to get them back. Therefore you have to change the logistics when you have several machines with only one driver. So you have new challenges. There are ideas in academia about swarm robots, which could harvest. But at the moment, especially in the harvesting process, we have lots of very messy goods. We have to transport many tons and that is not suited to tiny robots, so they should be big robots.

Per Sjöborg:  Then they become dangerous robots.

Hans-Peter Grothaus:  They will become more dangerous for man and then you have new challenges.

Per Sjöborg:  As you’ve pointed out, even if they were autonomous in the field, you’d still have to bring them into the field.

Hans-Peter Grothaus:  You have to bring them and maintain them – you have to recharge them when they’re electrically powered. So, if you’re monitoring; if you have a small robot which monitors the health of plants, for example, that could drive around and give us this information.

Per Sjöborg:  “Do I need to add more fertilizer or water or pesticides?” It could do those kind of things.

Hans-Peter Grothaus:  Yes it could also do fertilizer and pesticides. But, as a first step, it would just give information.

Per Sjöborg:  Yes, to determine if we need to.

Hans-Peter Grothaus:  If we need to, and then give this information to the process.

Per Sjöborg:  So, for instance, it could go over a field and detect where there are a lot of weeds, to see if more pesticides are needed or if the grain isn’t developing properly and needs more fertilizer. It could also give you advanced information about where the crop hasn’t grown.

Hans-Peter Grothaus:  Yes, and bear in mind that these vehicles would travel very slowly, and they have a lot of time. They’d make maps of the field. Then, the sprayer or the fertilizer machine, with bags of material or fertilizer, would go in at high speed, and it would have an exact map of where to spray.

Per Sjöborg:  That would take the optimization we talked about in the beginning a step further; it wouldn’t only rely on the data you had last year, it would actually look at the field and base the optimization on how it appears now.

Hans-Peter Grothaus:  There are different systems just to decouple the processes.

Per Sjöborg:  Very interesting. So, where do you see the future? What are you working on in your secret labs right now? Where is the cutting edge of infield agricultural operation?

Hans-Peter Grothaus:  The first step is more autonomy in the infield process, in the logistics. We have installed high capacity in the machines but sometimes, in the logistic processes, there is a reason for the gaps so the farmer does not get the installed capacity. Aside from the infield logistics, you also have outfield logistics. So, where there are no roads, the normal navigation system doesn’t always have the right information for our surroundings.

Per Sjöborg:  No, it’s not on a paved road, you’re out there in the field and the maps aren’t very accurate.

Hans-Peter Grothaus:  Yes, and it doesn’t know where the right spot is and where there are obstacles like electricity wires, telephone cables and trees. This information is not collected today and it could help to make it available, to make plans for the outfield logistics.

Per Sjöborg:  Yes, and entrance and exit points for a field. The system could also tell you that a point is placed incorrectly and we could optimize much better.

Hans-Peter Grothaus:  Yes.

Per Sjöborg:  Then you could take the information you gathered and optimize and even adapt to that. Yeah, very interesting.

Hans-Peter Grothaus:  You can also simulate processes and you can use your simulation to educate the driver. Because there are only six to eight weeks in the harvesting period, and, for the rest of the year, the farmers aren’t harvesting and the drivers are not trained, so they need a couple of days to get into the process again.

Per Sjöborg:  They’re better at the end of the harvest than they are in the beginning. It’s like going on a skiing holiday, when it was a couple of years ago that you last skied, and you’re a much better skier the last day of your holiday than the first. But if we could train someone properly ahead of time, we could optimize this critical window of opportunity we have at harvesting time.

Hans-Peter Grothaus:  We have a certain period where we can do the work but we could use historical data and simulation to train the drivers.

Per Sjöborg:  Perfect. Thank you very much for taking the time to do the interview.

Hans-Peter Grothaus:  Thank you, it was a pleasure for me.

All audio interviews are transcribed and edited for clarity with great care, however, we cannot assume responsibility for their accuracy.

Link to audio file (27:49)

In this episode Per Sjoborg speaks with Henrik Schunk about his company’s work in gripping technology, modular robotics and dexterous manipulation. They then look at service robotics, which was the focus of the SCHUNK Expert Days in Hausen, Germany.

https://www.youtube.com/watch?v=-Om5bPgsKT8

Henrik A. Schunk
After his studies at the Technical Universities in Kaiserslautern and Dresden, Henrik A. Schunk joined the company SCHUNK GmbH & Co. As Managing Partner since 2001, he was initially responsible for the German Sales area, and then became the Head of the Business Developments worldwide.

Today, he is responsible in the management for the business area gripping systems, auto-mated solutions, and mobile gripping systems. Since July 2010, he is also the chairman of the European Robotics Association EUnited Robotics.

Links:

• Download mp3 (14.8 MB)
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS
• SCHUNK homepage

Link to audio file (33:13)

In today’s episode Per Sjöborg speaks with Rezia Molfino from the PMAR group at University of Genova about how all robots are service robots, it’s just that they serve people in different ways in different situations. We hear about some of the many interesting projects she is working on, ranging from challenging manufacturing problems in thin sheet machining (SwarmItFIX) and the textile industry (clopema), to assisted-living vehicles for use in an urban environment (Picav) and do-it-yourself, practical equipment for demining war zones (Locastra).

SwarmItFIX

CloPeMa

Picav

Locostra

https://www.youtube.com/watch?v=BmjDjW8SxqY

Rezia Maria Molfino
Rezia Maria Molfino is Professor at the University of Genova, where she specializes in robot mechanics, industrial and service robotics, control of mechanical systems, flexible automation, and methods in mechanical design and programming for robotics.

Among her many advisory roles, she serves as an expert for LEIT-NMP’s Advisory Group (Horizon 2020), as President of the Italian Association of Robotics and Automation, and as National Coordinator at the International Federation of Robotics.

She has authored some 200 publications in the areas of robotics, intelligent manufacturing systems and automation, holds several patents on robotic equipment, and is involved in numerous robotics research projects, including: PRIDE, MiniPKM, Roboclimber, SBC, Smartwire, EUROShoE, SAFERDRILL, LEAPFROG, AUTORECON, SwarmItFIX (coord), PICAV (coord), FURBOT (coord) and Networks like OSNET, EURON, CLAWAR.
Links:

• Download mp3 (20.2MB)
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS
• Rezia Molfino’s Website
• Locostra
• The PMAR group

Link to audio file (25:51)

In today’s episode Per Sjöborg speaks with Jan Westerhues, Investment Partner with Robert Bosch Venture Capital, about how they fund robotics companies. As a stepping stone towards their long-term goal of building autonomous cars, they are currently investing in a wide range of robotics technologies with real world applications. Westerhues talks about when in a project’s life they can help, and what to expect throughout their involvement. He also promises exciting news in the coming months, so stay tuned.

Jan Westerhues
Jan Westerhues is Investment Partner with Robert Bosch Venture Capital in Frankfurt, Germany, where he is responsible for robotic investments. He is also a board member/observer at Aethon (USA), as well as at a soon-to-be-disclosed low cost autonomous navigation company in the EU. RBVC is the corporate venture arm of Robert Bosch GmbH, one of the largest private conglomerates with over 50 billion Euros sales. The VC fund invests in start-up companies  worldwide either directly or via Venture Capital Funds. Investment focus is technology companies that fit Bosch’s current and future business. RBVC provides capital for minority stakes in start-ups from early to expansion stage. Prior to this position, Jan was responsible for Bosch Corporate Strategy in the Asia Pacific region, based in Shanghai, China. He gathered extensive experience in strategy consulting before joining the Bosch Group. Jan holds a degree in engineering and business administration (Dipl.-Wirtsch.-Ing.) from the Technical University of Darmstadt, Germany and is a CFA charter holder. He speaks German, English, and Spanish fluently and has a basic knowledge of Mandarin.

Links:

• Download mp3 (15.3MB)
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS
• Robert Bosch Venture Capital

Link to audio file (41:58)

In today’s episode Per Sjöborg speaks with Giulio Sandini, director of the Robotics, Brain and Cognitive Sciences department at the Italian Institute of Technology (IIT), about how he eventually came to study robotics after starting out as a vision scientist in the field of bioengineering. They talk about why interdisciplinary work is important to robotics, and how diverse teams of engineers, biologists, psychologists, mathematicians, physicists, and medical doctors can learn from each other; Sandini follows up with several examples of interdisciplinary success at the IIT including the iCub and COMAN humanoid platforms, the HyQ quadruped, and their work in rehabilitation robotics.

Giulio Sandini

Giulio Sandini is Director of Research at the Italian Institute of Technology and full professor of bioengineering at the University of Genoa. After his graduation in Electronic Engineering (Bioengineering) at the University of Genova in 1976 he was research fellow and assistant professor at the Scuola Normale Superiore in Pisa until 1984. During this period, working at the Laboratorio di Neurofisiologia of the CNR, he investigated aspects of visual processing at the level of single neurons as well as aspects of visual perception in human adults and children. He has been Visiting Research Associate at the Department of Neurology of the Harvard Medical School in Boston. After his return to Genova in 1984 as associate professor he founded the Laboratory for Integrated Advanced Robotics. In 1996 he was Visiting Scientist at the Artificial Intelligence Lab of MIT.

Since 2006 he is Director of Research at the Italian Institute of Technology where he has established and is currently directing the Robotics, Brain and Cognitive Sciences department. RBCS is a multidisciplinary environment composed of researchers with different backgrounds (engineers, biologists, psychologists, mathematicians, physicists, medical doctors) sharing “human centered” Scientific and Technological interests along three streams of research: Humanoid Cognition, Human Behavior and Biomechanics, Brain Machine Interface.

Links:

• Download mp3 (20.8MB)
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS
• Giulio Sandini’s Website
• Italian Institute of Technology

If you liked this podcast, you may also be interested in these articles:

• Making an iCub robot “clever”
• Robot Companions: Flagship funding proposal for 1 billion euro submitted
• ICRA 2013 robot photo essay: “Anthropomatics – Technologies for humans”
• From the mouths of babes (or in this case, iCubs)
• Russia looks inward and outward at Moscow’s 2013 Open Innovations Forum

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Link to audio file (37:33)

In today’s episode, Per Sjöborg speaks with Lynne Parker from the University of Tennessee about her work with robot teams. Her lab is developing techniques to get robots to team up to complete tasks that they individually could not. The robots share data from sensors to extend their perception or share capabilities to perform particular tasks. This approach is essential to increase the efficiency, potential and reliability of multi-robot systems.

Ideally, multi-robot systems should be able to cope with different types of robots joining or leaving the team, and tasks changing over time. While most approaches would require a roboticist to manually predefine how the robot team accomplishes its task, Parker automates this process using smart algorithms and software architectures. This makes the design of multi-robot systems more manageable in the long run since one framework can be used in different situations.

Dr. Parker also works on integrating humans in the robot teams. This allows implicit communication between the robot and the human coworker to reduce the workload on the human and make the interaction more efficient.

In the long term, she hopes her research in multi-robot systems will enable applications in security, surveillance, and reconnaissance; planetary exploration; search and rescue; cleanup of hazardous waste; mining; construction; automated manufacturing; industrial/household maintenance; and nuclear power plant decommissioning.

 
Lynne Parker
Lynne E. Parker is Professor and Associate Head in the Department of Electrical Engineering and Computer Science at The University of Tennessee-Knoxville (UTK). At UTK, she is the founder and director of the Distributed Intelligence Laboratory, which performs research in multi-robot systems, sensor networks, machine learning, and human-robot interaction.

Parker is a leading international researcher in the field of distributed multi-robot systems. She has published over 135 articles in the areas of mobile robot cooperation, human-robot cooperation, sensor networks, robotic learning, intelligent agent architectures, and robot navigation. These publications include five edited books on the topic of distributed robotics.

She is the Editor-in-Chief of the IEEE Robotics Automation Society’s Conference Editorial Board, Senior Editor of IEEE Transactions on Robotics, Associate Editor of IEEE Intelligent Systems Magazine and an Associate Editor of the Swarm Intelligence journal. She serves on numerous international conference program committees. She is also an elected member of the Administrative Committee of the IEEE Robotics and Automation Society.

Links:

• Download mp3 (21.2MB)
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS
• Lynne Parker’s Website
• The Distributed Intelligence Laboratory’s Website

Link to audio file (31:13)

Diana Saraceni is a Venture Capitalist at 360 Capital Partners. In this interview, she tells us about her first investment in robotics 3 years ago with Invendo Medical, and her views on how the market has changed since then. Hardware is now perceived as less risky, even though it is more challenging to scale than software. Recent success stories have further helped promote VC funding in robotics.

Saraceni discusses the importance of the founding team, as well as their advisors, for the success of a company. Finally, she shares her view on open source vs. proprietary technology from a venture capitalist’s perspective.

Diana Saraceni
Diana Saraceni is a partner at 360 Capital Partners, a Pan-European Venture Capital firm. Before becoming a Venture Capitalist in 2001, she was Senior Advisor at Lazard Investment Banking. At Lazard she was part of the Technology Team and primarily worked on M&A transactions and IPOs. Prior to Lazard, Saraceni spent several years as strategic consultant with A.T.Kearney in Milan and London. She was/is part of several national and international start-up selection committees and holds a degree in Engineering from the University of Rome and an MBA from LUISS.

Links:

• Download mp3 (19.5MB)
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS
• Diana Saraceni
• 360 Capital Partners
• Invendo Medical

Link to audio file (33:23)

In this episode, Per talks to Pericle Salvini from Scuola Superiore Sant’Anna about his work with social, ethical and legal issues in robotics. He tells us about the Robolaw project that will provide advice to the European Union when it creates laws concerning robotics. Finally, we discuss how you can contribute to this important work.

Pericle Salvini
Pericle Salvini graduated in Foreign Languages and Literatures from the University of Pisa in 2000 with a thesis on ‘Theatre and Technologies’. In 2005, he completed a Master in Theatre Studies at Lancaster University (UK), where he delved into the telepresence artworks of E. Kac, R. Ascott and P. Sermon. In 2008, he received his PhD in Biorobotics Science and Engineering from IMT Lucca Institute for Advanced Studies and Scuola Superiore Sant’Anna (Italy). In his PhD thesis he studied robot design and ethical, legal and social implications. He is currently a research fellow at the BioRobotics Institute of Scuola Superiore Sant’Anna, Pisa, Italy. His main research interests are in the fields of Human-Robot Interaction (design, human factor and social acceptance of robots), and technoethics (ethical, legal and social implications of robotics research and applications). He is also involved in activities concerning the use of robots in education and art. He is currently co-chair of the Human Robot Interaction and Communication Technical Committee of RAS-IEEE and project manager of the Robolaw project.

Links:

• Download mp3 (22.5MB)
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS
• Pericle Salvini’s website
]]> https://robohub.org/robots-insect-inspired-navigation/ Fri, 22 Feb 2013 18:36:07 +0000 http://www.robotspodcast.com/?p=3128

Link to audio file (27:47)

In this episode Per talks to Michael Mangan from the University of Edinburgh about using robotics to study and replicate insect behaviour. Mangan describes his studies of desert ants, that are able to accurately navigate arduous environments despite having a very small brain (less than 400 000 neurons). This is an interesting problem as the desert environment is very challenging, it is too hot for pheromone navigation and nearly featureless, making visual navigation difficult.

Michael Mangan
Michael Mangan started by training as an avionics engineer at the University of Glasgow, later deciding to specialize in robotics after taking a course. At that time he was particularly inspired by some of the biorobotics projects in the press such as MIT’s Robot Tuna and Penguin Boat. He was very interested in this approach promising improved performance for engineering tasks by taking inspiration from biological systems solving similar problems.

Keen to work in this area he then moved to the Insect Robotics Lab, at the University of Edinburgh to undertake a PhD with Prof. Barbara Webb (see previous podcast interview). This lab combines robotics techniques with animal behavioural experiments in a synergistic loop aimed at revealing how these organisms achieve such impressive behaviors, despite their limited neural hardware and often low-resolution sensory systems. Revealing the parsimonious techniques used by these animals may then allow us to apply them to robotic systems.

Mangan’s current research focuses on the navigational abilities of desert ants. These ants scavenge for food over long distances despite searing surface temperatures when pheromone trails evaporate too quickly to use for guidance. Instead the ants rely mainly on visual cues for guidance. He has recently documented the impressive individual route following behavior of desert ants in southern Spain, and mapped their habitat for the first time. This has allowed the first rigorous testing of robotic and biologically plausible models of navigation in the ant world, as viewed by the ant.

Mangan is currently constructing these virtual worlds for public use and they will be available from www.AntNav.org. This webpage is currently under development but he hopes to have initial data uploaded soon, so stay tuned.

Links:

• Download mp3 (17.7MB)
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS
• Michael Mangan’s Website
• AntNav Website
]]> https://robohub.org/robots-sphero/ Fri, 14 Dec 2012 13:11:38 +0000 http://www.robotspodcast.com/?p=3015

Link to audio file (25:10)

In today’s episode, Per Sjoborg speaks with Ian Bernstein about the hugely successful Sphero robot and the company he co-founded, Orbotix.

Ian Bernstein
Ian Bernstein is cofounder of Orbotix which produces Sphero, a small ball-like robot which can be controlled from your smartphone. Launched one year ago, the robot has been hugely successful and is now available in the Apple store, Brookstone and Target to only cite a few. Key to their success has been a growing community of users writings apps for and making videos of their robots. Demonstrations include swarm control using the kinect or swimming Spheros augmented with “fish tails”.

Bernstein has been a hardware tinkerer and robot enthusiasts since age 12 when he attended the International BEAM Robotics Games (see Mark Tilden ROBOTS interviews from 2008 and 2012). He then studied at New Mexico Tech and Colorado State University, all while working at Road Narrows Robotics and running his own web-development company.

In 2010, Bernstein and Adam Wilson (also co-founder) joined a 3-month program at a startup accelerator called Techstars that led to their new company. Bernstein tells us about finding funding for hardware companies, setting up a production line and certifications needed to launch a product.

Links:

• Download mp3 (15.6MB)
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS
• Sphero website
]]> https://robohub.org/robots-connectors-modular-robots/ Fri, 18 Nov 2011 12:30:27 +0000 http://www.robotspodcast.com/?p=2063

Link to audio file (44:00)

In today’s show we hear from our new collaborator, Per Sjoborg who is the founder of Flexibility Envelope, a blog on self-reconfiguring modular robotics. He speaks to Martin Nilsson from the Swedish Institute of Computer Science about his experience in making snake robots and connectors for modular robots.

Martin Nilsson

Martin Nilsson is Associate Professor at the Swedish Institute of Computer Science.

He presents his work on self-reconfigurable modular robots done as part of the DRAGON (Distributed Real-time Autonomously Guided OrgaNisms) project. His snake inspired robot is composed of a set of modules and DRAGON joints that enable the robot to physically connect and disconnect, share energy and communicate. He tells us about challenges in building such a robot, including making smart mechanical docking systems, integrating all the functional requirements of a joint in a single mobile structure, and using Model Predictive Control to generate robot motion.

In his current work, Nilsson is focussing on integration sensor readings to obtain precise motor control. As inspiration, and in collaboration with Neurophysiologists, he looks at how the cerebellum is able to fuse proprioceptive sensing and touch to achieve precise motions in humans.



Links:

• Download mp3 (23.1 MB)
• Subscribe to Robots using iTunes
• Subscribe to Robots using RSS
• Flexibility Envelope Blog
• Martin Nilsson’s homepage
]]>