Tag Archives: gets
#439153 OTTO Motors’ Biggest AMR Gets ...
Over the last few weeks, we’ve posted several articles about the next generation of warehouse manipulation robots designed to handle the non-stop stream of boxes that provide the foundation for modern ecommerce. But once these robots take boxes out of the back of a trailer or off of a pallet, there are yet more robots ready to autonomously continue the flow through a warehouse or distribution center. One of the beefiest of these autonomous mobile robots is the OTTO 1500, which is called the OTTO 1500 because (you guessed it) it can handle 1500 kg of cargo. Plus another 400kg of cargo, for a total of 1900 kg of cargo. Yeah, I don’t get it either. Anyway, it’s undergone a major update, which is a good excuse for us to ask OTTO CTO Ryan Gariepy some questions about it.
The earlier version, also named OTTO 1500, has over a million hours of real-world operation, which is impressive. Even more impressive is being able to move that much stuff that quickly without being a huge safety hazard in warehouse environments full of unpredictable humans. Although, that might become less of a problem over time, as other robots take over some of the tasks that humans have been doing. OTTO Motors and Clearpath Robotics have an ongoing partnership with Boston Dynamics, and we fully expect to see these AMRs hauling boxes for Stretch in the near future.
For a bit more, we spoke with OTTO CTO Ryan Gariepy via email.
IEEE Spectrum: What are the major differences between today’s OTTO 1500 and the one introduced six years ago, and why did you decide to make those changes?
Ryan Gariepy: Six years isn’t a long shelf life for an industrial product, but it’s a lifetime in the software world. We took the original OTTO 1500 and stripped it down to the chassis and drivetrain, and re-built it with more modern components (embedded controller, state-of-the-art sensors, next-generation lithium batteries, and more). But the biggest difference is in how we’ve integrated our autonomous software and our industrial safety systems. Our systems are safe throughout the entirety of the vehicle dynamics envelope from straight line motion to aggressive turning at speed in tight spaces. It corners at 2m/s and has 60% more throughput. No “simple rectangular” footprints here! On top of this, the entire customization, development, and validation process is done in a way which respects that our integration partners need to be able to take advantage of these capabilities themselves without needing to become experts in vehicle dynamics.
As for “why now,” we’ve always known that an ecosystem of new sensors and controllers was going to emerge as the world caught on to the potential of heavy-load AMRs. We wanted to give the industry some time to settle out—making sure we had reliable and low-cost 3D sensors, for example, or industrial grade fanless computers which can still mount a reasonable GPU, or modular battery systems which are now built-in view of new certifications requirements. And, possibly most importantly, partners who see the promise of the market enough to accommodate our feedback in their product roadmaps.
How has the reception differed from the original introduction of the OTTO 1500 and the new version?
That’s like asking the difference between the public reception to the introduction of the first iPod in 2001 and the first iPhone in 2007. When we introduced our first AMR, very few people had even heard of them, let alone purchased one before. We spent a great deal of time educating the market on the basic functionality of an AMR: What it is and how it works kind of stuff. Today’s buyers are way more sophisticated, experienced, and approach automation from a more strategic perspective. What was once a tactical purchase to plug a hole is now part of a larger automation initiative. And while the next generation of AMRs closely resemble the original models from the outside, the software functionality and integration capabilities are night and day.
What’s the most valuable lesson you’ve learned?
We knew that our customers needed incredible uptime: 365 days, 24/7 for 10 years is the typical expectation. Some of our competitors have AMRs working in facilities where they can go offline for a few minutes or a few hours without any significant repercussions to the workflow. That’s not the case with our customers, where any stoppage at any point means everything shuts down. And, of course, Murphy’s law all but guarantees that it shuts down at 4:00 a.m. on Saturday, Japan Standard Time. So the humbling lesson wasn’t knowing that our customers wanted maintenance service levels with virtually no down time, the humbling part was the degree of difficulty in building out a service organization as rapidly as we rolled out customer deployments. Every customer in a new geography needed a local service infrastructure as well. Finally, service doesn’t mean anything without spare parts availability, which brings with it customs and shipping challenges. And, of course, as a Canadian company, we need to build all of that international service and logistics infrastructure right from the beginning. Fortunately, the groundwork we’d laid with Clearpath Robotics served as a good foundation for this.
How were you able to develop a new product with COVID restrictions in place?
We knew we couldn’t take an entire OTTO 1500 and ship it to every engineer’s home that needed to work on one, so we came up with the next best thing. We call it a ‘wall-bot’ and it’s basically a deconstructed 1500 that our engineers can roll into their garage. We were pleasantly surprised with how effective this was, though it might be the heaviest dev kit in the robot world.
Also don’t forget that much of robotics is software driven. Our software development life cycle had already had a strong focus on Gazebo-based simulation for years due to it being unfeasible to give every in-office developer a multi-ton loaded robot to play with, and we’d already had a redundant VPN setup for the office. Finally, we’ve always been a remote-work-friendly culture ever since we started adopting telepresence robots and default-on videoconferencing in the pre-OTTO days. In retrospect, it seems like the largest area of improvement for us for the future is how quickly we could get people good home office setups while amid a pandemic. Continue reading
#439110 Robotic Exoskeletons Could One Day Walk ...
Engineers, using artificial intelligence and wearable cameras, now aim to help robotic exoskeletons walk by themselves.
Increasingly, researchers around the world are developing lower-body exoskeletons to help people walk. These are essentially walking robots users can strap to their legs to help them move.
One problem with such exoskeletons: They often depend on manual controls to switch from one mode of locomotion to another, such as from sitting to standing, or standing to walking, or walking on the ground to walking up or down stairs. Relying on joysticks or smartphone apps every time you want to switch the way you want to move can prove awkward and mentally taxing, says Brokoslaw Laschowski, a robotics researcher at the University of Waterloo in Canada.
Scientists are working on automated ways to help exoskeletons recognize when to switch locomotion modes — for instance, using sensors attached to legs that can detect bioelectric signals sent from your brain to your muscles telling them to move. However, this approach comes with a number of challenges, such as how how skin conductivity can change as a person’s skin gets sweatier or dries off.
Now several research groups are experimenting with a new approach: fitting exoskeleton users with wearable cameras to provide the machines with vision data that will let them operate autonomously. Artificial intelligence (AI) software can analyze this data to recognize stairs, doors, and other features of the surrounding environment and calculate how best to respond.
Laschowski leads the ExoNet project, the first open-source database of high-resolution wearable camera images of human locomotion scenarios. It holds more than 5.6 million images of indoor and outdoor real-world walking environments. The team used this data to train deep-learning algorithms; their convolutional neural networks can already automatically recognize different walking environments with 73 percent accuracy “despite the large variance in different surfaces and objects sensed by the wearable camera,” Laschowski notes.
According to Laschowski, a potential limitation of their work their reliance on conventional 2-D images, whereas depth cameras could also capture potentially useful distance data. He and his collaborators ultimately chose not to rely on depth cameras for a number of reasons, including the fact that the accuracy of depth measurements typically degrades in outdoor lighting and with increasing distance, he says.
In similar work, researchers in North Carolina had volunteers with cameras either mounted on their eyeglasses or strapped onto their knees walk through a variety of indoor and outdoor settings to capture the kind of image data exoskeletons might use to see the world around them. The aim? “To automate motion,” says Edgar Lobaton an electrical engineering researcher at North Carolina State University. He says they are focusing on how AI software might reduce uncertainty due to factors such as motion blur or overexposed images “to ensure safe operation. We want to ensure that we can really rely on the vision and AI portion before integrating it into the hardware.”
In the future, Laschowski and his colleagues will focus on improving the accuracy of their environmental analysis software with low computational and memory storage requirements, which are important for onboard, real-time operations on robotic exoskeletons. Lobaton and his team also seek to account for uncertainty introduced into their visual systems by movements .
Ultimately, the ExoNet researchers want to explore how AI software can transmit commands to exoskeletons so they can perform tasks such as climbing stairs or avoiding obstacles based on a system’s analysis of a user's current movements and the upcoming terrain. With autonomous cars as inspiration, they are seeking to develop autonomous exoskeletons that can handle the walking task without human input, Laschowski says.
However, Laschowski adds, “User safety is of the utmost importance, especially considering that we're working with individuals with mobility impairments,” resulting perhaps from advanced age or physical disabilities.
“The exoskeleton user will always have the ability to override the system should the classification algorithm or controller make a wrong decision.” Continue reading