BMW says it will deploy a fleet of 40 cars in late 2017

Bumping up the timetables, BMW has declared it will have a fleet of 40 self-driving series 7 cars, using BMW’s technology combined with MobilEye and Intel. Intel has recently been making a push to catch up to Nvidia as a chipmaker supplier to automakers for self-driving. It hasn’t quite said what the cars will do, but they will be trying lots of different roads. So far BMW has mostly been developing its own tech. More interesting has been their announcement of plans to sell rides via their DriveNow service. This was spoken of a year ago but not much more has been said.

https://www.youtube.com/watch?v=sqRkYemvjr8

Intel also bought 15% of “HERE” the company formerly known as Navteq and Nokia. Last year, the German automakers banded together to buy HERE from Nokia and the focus has been on “HD” self-driving maps.

Hyundai, Delphi show off cars

There are demo cars out there from Delphi and a Hyundai Ioniq. Delphi’s car has been working for a while (it’s an Audi SUV) but recently they have also added a bunch of MobilEye sensors to it. Reports about the car are good, and they hope to have it ready by 2019, showing up in 2020 or 2021 cars on dealer lots.

Toyota sticks to concepts

Toyota’s main announcement is the Concept-i meant to show off some UI design ideas. It’s cute but still very much a car, though with all the silly hallmarks of a concept — hidden wheels, strangely opening doors and more.

Quanergy announces manufacturing plans for $250 solid state LIDAR

Quanergy (Note: I am on their advisory board) announced it will begin manufacturing this year of automotive grade $250 solid state LIDARs. Perhaps this will stop all the constant articles about how LIDAR is super-expensive and means that robocars must be super-expensive too. The first model is only a taste of what’s to come in the next couple of years as well.

New Ford has a sleeker design

Ford has become the US carmaker to watch (in addition to Tesla) with their announcement last year that they don’t plan to sell their robocars, only use them to offer ride service in fleets. They are the first and only carmaker to say this is their exclusive plan. Just prior to CES, Ford showed off a new test model featuring smaller Velodyne pucks and a more deliberate design.

I have personally never understood the desire to design robocars to “look like regular cars.” I strongly believe that, just like the Prius, riders in the early robocars will want them to look distinctive, so they can show off how they are in a car of the future. Ford’s carm based on the Fusion hybrid, is a nice compromise — clearly a robocar with its sensors, but also one of sleek and deliberate design.

Nvidia keeps its push

Nvidia has a new test car they have called BB8. (Do they have to licence that name?) It looks fairly basic, and they show a demo of it taking somebody for a ride with voice control, handling a lot of environments. It’s notable that at the end, the driver has to take over to get to the destination, so it doesn’t have everything, nor would we expect it. NVIDIA is pushing their multi-GPU board as the answer to how to get a lot of computing power to run neural networks in the car.

What’s coming

Announcements are due tomorrow from Nissan and probably others. I’ll report Friday from the show floor. See you there!

If you liked this post on robocars, you’ll also enjoy these articles:

• What if the city ran Waze and you had to obey it? Could this cure congestion?
• What are the right disability rules for robotaxis?
• Google car is now Waymo
• Comma.ai cancels comma-one add-on box after threats from NHTSA
• Robocar news: Comma One goes open source, creating simulations for robocars in New Zealand earthquakes

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

Michael Aeberhard: Automated Driving with ROS at BMW. Source: OSRF Blog

ROSCon is an annual conference focused on ROS, the Robot Operating System. Every year, hundreds of ROS developers of all skill levels and backgrounds, from industry to academia, come together to teach, learn, and show off their latest projects.

Here is the next set of posts from the OSRF blog, along with videos.

Amit Moran (Intel): Introducing ROS-RealSense: 3D Empowered Robotics Innovation Platform

While Intel is best known for making computer processors, the company is also interested in how people interact with all of the computing devices that have Intel inside. In other words, Intel makes brains, but they need senses to enable those brains to understand the world around them. Intel has developed two very small and very cheap 3D cameras (one long range and one short range) called RealSense, with the initial intent of putting them into devices like laptops and tablets for applications such as facial recognition and gesture tracking.

Robots are also in dire need of capable and affordable 3D sensors for navigation and object recognition, and fortunately, Intel understands this, and they’ve created the RealSense Robotics Innovation Program to help drive innovation using their hardware. Intel itself isn’t a robotics company, but as Amit explains in his ROSCon talk, they want to be a part of the robotics future, which is why they prioritized ROS integration for their RealSense cameras.

A RealSense ROS package has been available since 2015, and Intel has been listening to feedback from roboticists and steadily adding more features. The package provides access to the RealSense camera data (RGB, depth, IR, and point cloud), and will eventually include basic computer vision functions (including plane analysis and blob detection) as well as more advanced functions like skeleton tracking, object recognition, and localization and mapping tools.

Intel RealSense 3D camera developer kits are available now, and you can order one for as little as $99.

Michael Aeberhard (BMW): Automated Driving with ROS at BMW

BMW has been working on automated driving for the last decade, steadily implementing more advanced features ranging from emergency stop assistance and autonomous highway driving to fully automated valet parking and 360° collision avoidance. Several of these projects were presented at the 2015 Consumer Electronics Show, and as it turns out, the cars were running ROS for both environment detection and planning.

BMW, being BMW, has no problem getting new research hardware. Their latest development platform is the 335I G. This model comes with an advanced driver assistance system based around cameras and radar. The car has been outfitted with four low-profile laser scanners and one long-range radar, but otherwise, it’s pretty close (in terms of hardware) to what’s available in production BMWs.

Why did BMW choose to move from their internally developed software architecture to ROS? Michael explains how ROS’ reputation in the robotics research community prompted his team to give it a try, and they were impressed with its open source nature, distributed architecture, existing selection of software packages, as well as its helpful community. “A large user base means stability and reliability,” Michael says, “because somebody else probably already solved the problem you’re having.” Additionally, using ROS rather than a commercial software platform makes it much easier for BMW to cooperate with universities and research institutions.

Michael discusses the ROS software architecture that BMW is using to do its autonomous car development, and shows how the software interprets the sensor data to identify obstacles and lane markings and do localization and trajectory planning to enable full highway autonomy, based on a combination of lane keeping and dynamic cruise control. BMW also created their own suite of RQT and rviz plugins specifically designed for autonomous vehicle development.

After about two years of experience with ROS, BMW likes a lot of things about it, but Michael and his team do have some constructive criticisms: message transport needs more work (although ROS 2 should help with this), managing configurations for different robots is problematic, and it’s difficult to enforce compliance with industry standards like ISO.

Jerry Towler (SwRI): Mapviz – An Extensible 2D Visualization Tool for Automated Vehicles

ROS already comes with a fantastic built-in visualization tool called rviz, so why would you want to use anything else? At Southwest Research Institute, Jerry Towler explains how they’ve created a new visualization tool called Mapviz that’s specifically designed for the kind of large-scale outdoor environments necessary for autonomous vehicle development. Specifically, Mapviz is able to integrate all of the sensor data that you need on top of a variety of two-dimensional maps, such as road maps or satellite imagery.

As an autonomous vehicle visualization tool, Mapviz works just like you’d expect that it would, which Jerry demonstrated with several demos at ROSCon. Mapviz shows you a top-down view of where your vehicle is, and tracks it across a basemap that seamlessly pulls image tiles at multiple resolutions from a wide variety of local or networked map servers, including Open MapQuest and Bing Maps. Mapviz is, of course, very plugin-friendly. You can add things like stereo disparity feeds, GPS fixes, odometry, grids, pathing data, image overlays, projected laser scans, markers (including textured markers) from most sensor types, and more. It can’t really handle three dimensional data (although it’ll do two-and-a-half dimensions via color gradients), but for interactive tracking of your vehicle’s navigation and path planning behavior, Mapviz should offer most of what you need.

For a variety of non-technical reasons, SwRI hasn’t been able to release all of its tools and plugins as open source quite yet, but they’re working on getting approval as fast as they can. They’re also in the process of developing even more enhancements for Mapviz, and you can keep up to date with the latest version of the software on GitHub.

Matt Vollrath and Wojciech Ziniew (End Point): ROS-Driven User Applications in Idempotent Environments

Matt Vollrath and Wojciech Ziniew work at an ecommerce consultancy called End Point, where they provide support for Liquid Galaxy; a product that’s almost as cool as it sounds. Originally an open source project begun by Google engineers on their twenty percent time, Liquid Galaxy is a data visualization system consisting of a collection of large vertical displays that wrap around you horizontally. The displays show an immersive (up to 270°) image that’s ideal for data presentations, virtual tours, Google Earth, or anywhere you want a visually engaging environment. Think events, trade shows, offices, museums, galleries, and the like.

Last year, End Point decided to take all of the ad hoc services and protocols that they’d been using to support Liquid Galaxy and move everything over to ROS. The primary reason to do this was ROS support for input devices: you can use just about anything to control a Liquid Galaxy display system, from basic touchscreens to Space Navigator 3D mice to Leap Motions to depth cameras. The modularity of ROS is inherently friendly to all kinds of different hardware.

Check out this week’s ROSCon15 video as Matt and Wojciech take a deep dive into their efforts in bringing ROS to bear for these unique environments.

Tom Moore: Working with the Robot Localization Package

Clearpath Robotics is best known for building yellow and black robots that are the research platforms you’d build for yourself; that is, if it wasn’t much easier to just get them from Clearpath Robotics. All of their robots run ROS, and Clearpath has been heavily involved in the ROS community for years. Now with Locus Robotics, Tom Moore spent seven months as an autonomy developer at Clearpath. He is the author and maintainer of the robot_localization ROS package, and gave a presentation about it at ROSCon 2015.

robot_localization is a general purpose state estimation package that’s used to give you (and your robot) an accurate sense of where it is and what it’s doing, based on input from as many sensors as you want. The more sensors that you’re able to use for a state estimate, the better that estimate is going to be, especially if you’re dealing with real-worldish things like unreliable GPS or hardware that flakes out on you from time to time. robot_localization has been specifically designed to be able to handle cases like these, in an easy to use and highly customizable way. It has state estimation in 3D space, gives you per-sensor message control, allows for an unlimited number of sensors (just in case you have 42 IMUs and nothing better to do), and more.

Tom’s ROSCon talk takes us through some typical use cases for robot_localization, describes where the package fits in with the ROS navigation stack, explains how to prepare your sensor data, and how to configure estimation nodes for localization. The talk ends with a live(ish) demo, followed by a quick tutorial on how to convert data from your GPS into your robot’s world frame.

The robot_localization package is up to date and very well documented, and you can learn more about it on the ROS Wiki.

Moritz Tenorth (Magazino): Maru and Toru — Item-Specific Logistics Solutions Based on ROS

It’s not sexy, but the next big thing for robots is starting to look like warehouse logistics. The potential market is huge, and a number of startups are developing mobile platforms to automate dull and tedious order fulfillment tasks. Transporting products is just one problem worth solving: picking those products off of shelves is another. Magazino is a German startup that’s developing a robot called Toru that can grasp individual objects off of warehouse shelves, a particularly tricky task that Magazino is tackling with ROS.

Moritz Tenorth is Head of Software Development at Magazino. In his ROSCon talk, Moritz describes Magazino’s Toru as “a mobile pick and place robot that works together with humans in a shared environment,” which is exactly what you’d want in an e-commerce warehouse. The reason that picking is a hard problem, as Moritz explains, is perception coupled with dynamic environments and high uncertainty: if you want a robot that can pick a wide range of objects, it needs to be able to flexibly understand and react to its environment; something that robots are notoriously bad at. ROS is particularly well suited to this, since it’s easy to intelligently integrate as much sensing as you need into your platform.

Magazino’s experience building and deploying their robots has given them a unique perspective on warehouse commercialization with ROS. For example, databases and persistent storage are crucial (as opposed to a focus on runtime), and real-time control turns out to be less important than being able to quickly and easily develop planning algorithms and reducing system complexity. Software components in the ROS ecosystem can vary wildly in quality and upkeep, although ROS-Industrial is working hard to develop code quality metrics. Magazino is also working on remote support and analysis tools, and trying to determine how much communication is required in a multi-robot system, which native ROS isn’t very good at.

Even with those (few) constructive criticisms in mind, Magazino says that ROS is a fantastic way to quickly iterate on both software and hardware in parallel, especially when combined with 3D printed prototypes for testing. Most importantly, Magazino feels comfortable with ROS: it has a familiar workflow, versatile build system, flexible development architecture, robust community that makes hiring a cinch, and it’s still (somehow) easy to use.

Michael Ferguson (Fetch Robotics): Accelerating Your Robotics Startup with ROS

Michael Ferguson spent a year as a software engineer at Willow Garage, helping rewrite the ROS calibration system, among other projects. In 2013, he co-founded Unbounded Robotics, and is currently the CTO of Fetch Robotics. At Fetch, Michael is one of the primary people responsible for making sure that Fetch’s robots reliably fetch things. Mike’s ROSCon talk is about how to effectively use ROS as an integral part of your robotics business, including best practices, potential issues to avoid, and how you should handle open source and intellectual property.

Because of how ROS works, much of your software development (commercial or otherwise) is dependent on many external packages. These packages are constantly being changed for the better — and sometimes for the worse — at unpredictable intervals that are completely out of your control. Using continuous integration, consisting of systems that can handle automated builds, testing, and deployment, can help you catch new problems as early as possible. Michael also shares that a useful way to avoid new problems is to not immediately switch over to new software as soon as they are available: instead, stick with long-term support releases, such as Ubuntu 14.04 and ROS Indigo.

While the foundation of ROS is built on open source, using ROS doesn’t mean that all of the software magic that you create for your robotics company has to be given away for free. ROS supports many different kinds of licenses, some of which your lawyers will be more happy with than others, but there are enough options with enough flexibility that it doesn’t have to be an issue. Using Fetch Robotics as an example, Mike discusses what components of ROS his company uses in their commercial products, including ROS Navigation and MoveIt. With these established packages as a base, Fetch was able to quickly put together operational demos, and then iterate on an operating platform by developing custom plugins optimized for their specific use cases.

When considering how to use ROS as part of your company, it’s important to look closely at the packages you decide to incorporate, to make sure that they have a friendly license, good documentation, recent updates, built-in tests, and a standardized interface. Keeping track of all of this will make your startup life easier in the long run. As long as you’re careful, relying on ROS can make your company more agile, more productive, and ready to make a whole bunch of money off of the future of robotics.

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

• ROSCon 2015 recap and videos – Part 4
• ROSCon 2015 recap and videos – Part 3
• ROSCon 2015 recap and videos – Part 2
• ROSCon 2015 recap and videos – Part 1

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

Delphi has made waves by undertaking a road trip from San Francisco to New York in their test car, which is equipped with an impressive array of sensors. The trip is now underway, and on their page you can see lots of videos of the vehicle along the trek.

The Delphi vehicle is one of the most sensor-laden vehicles out there, and that’s good. In spite of all those who make the rather odd claim that they want to build robocars with fewer sensors, Moore’s Law and other principles teach us that the right procedure is to throw everything you can at the problem today, because those sensors will be cheap when it comes time to actually ship — especially for those who say they won’t ship for a decade.

At the same time, according to Kristen Kinley at Delphi, their test involves mostly highway driving, with very minimal urban street driving. They are attempting off-map driving, which is possible on highways due to their much simpler environment. Like all testing projects these days, there are safety drivers in the cars ready to intervene at the first sign of a problem.

Delphi is doing a small amount of dedicated short-range communications (DSRC) vehicle-to-infrastructure testing as well, though this is only done in Mountain View where they used some specially installed roadside radio infrastructure equipment.

Delphi is doing the right thing here — getting lots of miles and different roads under their belt. This is Google’s giant advantage today. Based on Google’s announcements, they have more than a million miles of testing in the can, and that makes a big difference.

Hype and reality of Tesla’s autopilot announcement

Telsa has announced they will do an over-the-air upgrade of car software in a few months to add autopilot functionality to existing models that have sufficient sensors. This autopilot is the “supervised” class of self driving that I warned may end up being seen as boring. The press have treated this as something immense, but as far as I can tell, this is similar to products built by Mercedes, BMW, Audi and several other companies, and even sold in the market (at least for traffic jams) for a couple of years now.

The other products have shied away from doing full highway speed in commercial products, though rumours exist of it being available in commercial cars in Europe. What is special about Tesla’s offering is that it will be the first car sold in the US to do this at highway speed, and they may offer supervised lane change as well. It’s also interesting that, since they have been planning this for a while, it will come as a software upgrade to people who bought their technology package earlier.

UK project budget rises to £100 million

What started with a £10 million pound prize in the UK has grown become over 100m in grants in the latest UK budget. While government research labs will not provide us with the final solutions, this money will probably create some very useful tools and results for the private players to exploit.

MobilEye releases their EyeQ4 chip

MobilEye from Jerusalem is probably the leader in automotive machine vision, and their new generation chip has been launched, but won’t show up in cars for a few years. It’s an ASIC packed with hardware and processor cores aimed at doing easy machine vision. My personal judgement is that this is not sufficient for robocar driving, but MobilEye wants to prove me wrong. (The EQ4 chip does have software to do sensor fusion with LIDAR and Radar, so they don’t want to prove me entirely wrong.) Even if they are not good enough on their own, ME chips offer a good alternate path for redundancy.

Chris Urmson gives a TED talk about the Google Car

Talks by Google’s team are rare — the project is unusual in trying to play down its publicity. I was not at TED, but reports from there suggest Chris did not reveal a great deal new, other than repeating his goal of having the cars be in practical service before his son turns 16. Of course, humans will be driving for a long time after robocars start becoming common on the roads, but it is true that we will eventually see teens who would have gotten a licence never get around to getting one. (Teens are already waiting longer to get their licences, so this is not a hard prediction.)

The war between DSRC and more wifi is heating up.

Two years ago, the FCC warned that since auto makers had not figured out how to benefit from the DSRC spectrum at 5.9ghz, it was time to repurpose it for unlicensed use, like more WiFi. There is now a bill to force this proposition.

I think this would be the best thing ever for V2V, and I outlined 2 years ago how doing V2V in phones instead of cars is far more likely to get success. Phones are replaced every two years with a whole new generation, and smartphones are already in 75% of cars in the USA. V2V, even under a mandate, won’t get close to that until after 2030, and by that time it will be 15-year-old technology, long obsolete.

Google Airbags on the outside

A Google patent for placing airbags of a sort on the outside of vehicles to protect others has gotten a lot of press. While I was working at Google, I started the push for this (though the design in the patent is not mine.) I’m glad to see more investigation of it, though. The reality, as everybody knows, is that perfection is not possible, so planning for the hopefully small number of inevitable accidents and mitigating them is the right strategy.

Nvidia

For the past year, Nvidia has been pitching their latest Tegra GPU in the mobile space. They argue that a general purpose chip with very high power, thanks to their giant research budget, will produce better results than a dedicated machine vision chip from a smaller company like MobilEye that takes several years between generations. As a result, they put a big focus on automotive and self-driving at their recent GPU conference. I was in Europe and unable to attend.

More news from China

Reports suggest that Chinese internet companies Baidu and Tencent (WeChat) are starting robocar projects. (Previously Baidu provided some maps for BMW’s projects.) I’ve spoken at a couple of events for Baidu and encouraged this direction. Details are scarce though, and there is some speculation this is more in the “smart” car direction (similar to Tesla) than a full robocar. We should note that the legal situation in China is a lot different, and these large companies might be able to easily get more advanced products to be declared legal well before those in other countries can.

BMW crash

A BMW driver testing night vision collision avoidance systems crashed into a police van in Stuttgart. This was not a robocar, and of course it is amusing that if you have a crash, you might as well do it into a speeding police van! It does raise issues about risks of testing things with safety drivers — in this case, perhaps the test driver was distracted by his testing and didn’t pay enough attention to the environment. Most of the supervised class vehicles being developed today require the driver to keep paying attention, something that Google has decided not to pursue.

This article first appeared on robocars.com.

A BMW stops on its own when the driver tries, full throttle, to drive it into the obstacle. It also won’t let you sideswipe obstacles. I have been in cars that do this before; this one seemed to stop more gently. Photo credit: Brad Templeton.

Day 3 at CES started with a visit to BMW’s demo. They were mostly test driving new cars like the i3 and M series cars, but for a demo, they made the i3 deliver itself along a planned corridor. It was a mostly stock i3 electric car with ultrasonic sensors — and the traffic jam assist disabled. When one test driver dropped off the car, they scanned it, and then a BMW staffer at the other end of a walled course used a watch interface to summon that car. It drove empty along the line waiting for test drives, and then a staffer got in to finish the drive to the parking spot where the test driver would actually get in, unfortunately.

Also on display were BMW’s collision avoidance systems in a much more equipped research car with LIDARs, Radar etc. This car has some nice collision avoidance. It has obstacle detection — the demo was to deliberately drive into an obstacle, but the vehicle hits the brakes for you. More gently than the Volvo I did this in a couple of years ago.

More novel is detection of objects you might hit from the side or back in low speed operations. If it looks like you might sideswipe or back into a parking column or another car, the vehicle hits the brakes on you (harder) to stop it from happening.

Insurers will like this — low speed collisions in parking lots are getting to be a much larger fraction of insurance claims. The high speed crashes get all the attention, but a lot of the payout is in low speed.

These roller skates capture energy in one part of your stroke and give it back to you in another lo let you go faster. Photo credit: Brad Templeton.

I concluded with a visit to my favourite section of CES — Eureka Park, where companies get small lower cost booths, with a focus on new technology. Also in the Sands were robotics, 3D printing, health, wearables and more — never enough time to see it all.

I have added 12 more photos to my gallery, with captions — check the last part out for notes on cool products I saw, from self-tightening belts and regenerating roller skates to phone-charging camping pots.

In a crowded back room at AUTOMATICA 2014 in Munich, the International Federation of Robotics (IFR) sponsored a panel of prominent robot users explaining their needs to a panel of executives representing all the prominent robot manufacturers.

The panel was moderated by Ken Fouhy of VDI Nachrichten, a German newspaper and web portal for technology news in the EU. The two robot users were Stefan Baginski representing BMW and Dr. Chia Day from Foxconn; the robot executives were Manfred Gundel from KUKA, Olaf Kramm from Fanuc, Stefan Lampa from ABB, Manfred Stern from Yaskawa and Jiegao Wang, from Estun Robotics.

The dialogue was spirited, direct, and kept on point by the moderator. It was particularly informative on three topics:

• Foxconn’s Dr. Day explained his company’s “million robot deployment” in context.
• BMW’s Baginski described situations that could add significant numbers to BMW’s robot population.
• The newly formed Chinese Robot Industry Alliance (CRIA) had a booth at AUTOMATICA and an executive from a Chinese robot manufacturer was on the user/maker panel, confirming that China is now the largest buyer of robots and their government is stimulating an in-country robotics industry.

FOXCONN AND THE MILLION ROBOTS

Dr. Day, VP and GM of Foxconn’s Shenzhen facilities, described in careful detail where robots are needed in their factories, what tasks they need to do, what hurdles there are, and, to some extent, what they are doing in-house. Their most immediate need is in core tasks associated with electronics assembly such as machining, polishing, painting, laser welding, die casting, palletizing and inspections. Automated assembly will come later he said. He described the major difference between an auto company and a consumer product manufacturer: the length of time production “lines” stay in play; car company lines often last for 7 years while most electronic production lines change yearly or more often. Hence the need for flexibility in deployment, cost efficiency, and ease of programming and re-programming.

Dr Day said that Foxconn has grown to 1.2 million workers and the logistics, turnover and ancillary problems and activities involved with that massive workforce had prompted Foxconn’s Chairman Terry Gou to say that Foxconn would deploy 1 million robots to help reduce and control the numbers, manage the problems better while increasing quality, and eliminate the dull, dirty and dangerous tasks that contributed to labor problems in the past.

He suggested that the media was more interested in the few instances of labor troubles than in the amazing technologies, capabilities and components the company provided and offered; that the media often incorrectly reported non-stories. For example, the flurry of stories about Google and Foxconn having an “agreement to cooperate” was unknown to him. He read more about it in the press than he heard about it from within Foxconn.

When queried about the fact that Foxconn has a line of robots that it manufactures, he explained that Foxbots were for internal use only and for material handling, for tool and die making, and for integrating from more costly purchased systems. They decided to make Foxbots solely for economic reasons. He also said that right now Foxconn utilized about 50,000 “traditional” robots, had an additional 50,000 automation devices that might be called robots, and about 500,000 not-robotic but intelligent automation devices mostly of its own creation. “Surprisingly forthright comments from a very interesting proponent of automated manufacturing,” said moderator Ken Fouhy.

BMW MAY INCREASE THEIR ROBOT POPULATION IF…

BMW’s Stefan Baginski talked about BMW’s experimenting with collaborative robots, particularly the ones from Universal Robots, for assembly and other tasks not previously robotized but that are ergonomically challenging for their employees. Thus far the human-robot interaction at their Spartanburg plant in South Carolina, and resulting productivity enhancement, has been successful and, if it continues, Baginski can foresee increasing BMW’s present robot population of 7,500 by a third to 10,000 within the next 3 years.

All the robot makers talked of co-robots and the issues involved but few had actual products for sale such as the ones from Universal Robots, particularly at Universal’s low price point ($30,000).

One fascinating question was whether the makers used human-robot-interaction robotics in their own manufacturing process. They all use robots to make robots. ABB said that they used their 2-armed FRIDA robot internally but for experimental purposes only; Olaf Kramm from Fanuc said:

“We have three times more robots in our production than humans. That’s the difference – we are able to provide high-quality sophisticated robots beyond automotive and general industry for the same price level that the others do when they produce in China,”

CHINA, CRIA AND CHINA-MADE ROBOTS

China has become the largest buyer of robotics buying one in five robots sold in 2013, a total of 36,560, according to the IFR. Rising wages, government stimulus, and growing competition from other emerging economies have been the drivers and 60% of the demand has come from the auto industry. There’s lots of room for this growth to continue: China had just 23 robots for every 10,000 people employed in manufacturing compared with 396 in Korea, 332 in Japan and 273 in Germany. Their brochure showing members of CRIA (China Robot Industry Alliance) was a hot take-away from their booth at AUTOMATICA.

Wang Jiegao, from China’s Estun Robotics, said:

“Labour costs are rising [in China] so there are a large number of enterprises that need automation. The challenge is to do that for small and medium-sized enterprises . . . as they have limited money for investment.”

HUMAN-ROBOT COLLABORATION

Hence the reason why robot manufacturers all over the world are focused on making their newer robots easier to program, operate, maintain, and that they be able to collaborate with humans. This slide from the presentation describes it well: