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#437667 17 Teams to Take Part in DARPA’s ...
Among all of the other in-person events that have been totally wrecked by COVID-19 is the Cave Circuit of the DARPA Subterranean Challenge. DARPA has already hosted the in-person events for the Tunnel and Urban SubT circuits (see our previous coverage here), and the plan had always been for a trio of events representing three uniquely different underground environments in advance of the SubT Finals, which will somehow combine everything into one bonkers course.
While the SubT Urban Circuit event snuck in just under the lockdown wire in late February, DARPA made the difficult (but prudent) decision to cancel the in-person Cave Circuit event. What this means is that there will be no Systems Track Cave competition, which is a serious disappointment—we were very much looking forward to watching teams of robots navigating through an entirely unpredictable natural environment with a lot of verticality. Fortunately, DARPA is still running a Virtual Cave Circuit, and 17 teams will be taking part in this competition featuring a simulated cave environment that’s as dynamic and detailed as DARPA can make it.
From DARPA’s press releases:
DARPA’s Subterranean (SubT) Challenge will host its Cave Circuit Virtual Competition, which focuses on innovative solutions to map, navigate, and search complex, simulated cave environments November 17. Qualified teams have until Oct. 15 to develop and submit software-based solutions for the Cave Circuit via the SubT Virtual Portal, where their technologies will face unknown cave environments in the cloud-based SubT Simulator. Until then, teams can refine their roster of selected virtual robot models, choose sensor payloads, and continue to test autonomy approaches to maximize their score.
The Cave Circuit also introduces new simulation capabilities, including digital twins of Systems Competition robots to choose from, marsupial-style platforms combining air and ground robots, and breadcrumb nodes that can be dropped by robots to serve as communications relays. Each robot configuration has an associated cost, measured in SubT Credits – an in-simulation currency – based on performance characteristics such as speed, mobility, sensing, and battery life.
Each team’s simulated robots must navigate realistic caves, with features including natural terrain and dynamic rock falls, while they search for and locate various artifacts on the course within five meters of accuracy to score points during a 60-minute timed run. A correct report is worth one point. Each course contains 20 artifacts, which means each team has the potential for a maximum score of 20 points. Teams can leverage numerous practice worlds and even build their own worlds using the cave tiles found in the SubT Tech Repo to perfect their approach before they submit one official solution for scoring. The DARPA team will then evaluate the solution on a set of hidden competition scenarios.
Of the 17 qualified teams (you can see all of them here), there are a handful that we’ll quickly point out. Team BARCS, from Michigan Tech, was the winner of the SubT Virtual Urban Circuit, meaning that they may be the team to beat on Cave as well, although the course is likely to be unique enough that things will get interesting. Some Systems Track teams to watch include Coordinated Robotics, CTU-CRAS-NORLAB, MARBLE, NUS SEDS, and Robotika, and there are also a handful of brand new teams as well.
Now, just because there’s no dedicated Cave Circuit for the Systems Track teams, it doesn’t mean that there won’t be a Cave component (perhaps even a significant one) in the final event, which as far as we know is still scheduled to happen in fall of next year. We’ve heard that many of the Systems Track teams have been testing out their robots in caves anyway, and as the virtual event gets closer, we’ll be doing a sort of Virtual Systems Track series that highlights how different teams are doing mock Cave Circuits in caves they’ve found for themselves.
For more, we checked in with DARPA SubT program manager Dr. Timothy H. Chung.
IEEE Spectrum: Was it a difficult decision to cancel the Systems Track for Cave?
Tim Chung: The decision to go virtual only was heart wrenching, because I think DARPA’s role is to offer up opportunities that may be unimaginable for some of our competitors, like opening up a cave-type site for this competition. We crawled and climbed through a number of these sites, and I share the sense of disappointment that both our team and the competitors have that we won’t be able to share all the advances that have been made since the Urban Circuit. But what we’ve been able to do is pour a lot of our energy and the insights that we got from crawling around in those caves into what’s going to be a really great opportunity on the Virtual Competition side. And whether it’s a global pandemic, or just lack of access to physical sites like caves, virtual environments are an opportunity that we want to develop.
“The simulator offers us a chance to look at where things could be … it really allows for us to find where some of those limits are in the technology based only on our imagination.”
—Timothy H. Chung, DARPA
What kind of new features will be included in the Virtual Cave Circuit for this competition?
I’m really excited about these particular features because we’re seeing an opportunity for increased synergy between the physical and the virtual. The first I’d say is that we scanned some of the Systems Track robots using photogrammetry and combined that with some additional models that we got from the systems competitors themselves to turn their systems robots into virtual models. We often talk about the sim to real transfer and how successful we can get a simulation to transfer over to the physical world, but now we’ve taken something from the physical world and made it virtual. We’ve validated the controllers as well as the kinematics of the robots, we’ve iterated with the systems competitors themselves, and now we have these 13 robots (air and ground) in the SubT Tech Repo that now all virtual competitors can take advantage of.
We also have additional robot capability. Those comms bread crumbs are common among many of the competitors, so we’ve adopted that in the virtual world, and now you have comms relay nodes that are baked in to the SubT Simulator—you can have either six or twelve comms nodes that you can drop from a variety of our ground robot platforms. We have the marsupial deployment capability now, so now we have parent ground robots that can be mixed and matched with different child drones to become marsupial pairs.
And this is something I’ve been planning for for a while: we now have the ability to trigger things like rock falls. They still don’t quite look like Indiana Jones with the boulder coming down the corridor, but this comes really close. In addition to it just being an interesting and realistic consideration, we get to really dynamically test and stress the robots’ ability to navigate and recognize that something has changed in the environment and respond to it.
Image: DARPA
DARPA is still running a Virtual Cave Circuit, and 17 teams will be taking part in this competition featuring a simulated cave environment.
No simulation is perfect, so can you talk to us about what kinds of things aren’t being simulated right now? Where does the simulator not match up to reality?
I think that question is foundational to any conversation about simulation. I’ll give you a couple of examples:
We have the ability to represent wholesale damage to a robot, but it’s not at the actuator or component level. So there’s not a reliability model, although I think that would be really interesting to incorporate so that you could do assessments on things like mean time to failure. But if a robot falls off a ledge, it can be disabled by virtue of being too damaged to continue.
With communications, and this is one that’s near and dear not only to my heart but also to all of those that have lived through developing communication systems and robotic systems, we’ve gone through and conducted RF surveys of underground environments to get a better handle on what propagation effects are. There’s a lot of research that has gone into this, and trying to carry through some of that realism, we do have path loss models for RF communications baked into the SubT Simulator. For example, when you drop a bread crumb node, it’s using a path loss model so that it can represent the degradation of signal as you go farther into a cave. Now, we’re not modeling it at the Maxwell equations level, which I think would be awesome, but we’re not quite there yet.
We do have things like battery depletion, sensor degradation to the extent that simulators can degrade sensor inputs, and things like that. It’s just amazing how close we can get in some places, and how far away we still are in others, and I think showing where the limits are of how far you can get simulation is all part and parcel of why SubT Challenge wants to have both System and Virtual tracks. Simulation can be an accelerant, but it’s not going to be the panacea for development and innovation, and I think all the competitors are cognizant those limitations.
One of the most amazing things about the SubT Virtual Track is that all of the robots operate fully autonomously, without the human(s) in the loop that the System Track teams have when they compete. Why make the Virtual Track even more challenging in that way?
I think it’s one of the defining, delineating attributes of the Virtual Track. Our continued vision for the simulation side is that the simulator offers us a chance to look at where things could be, and allows for us to explore things like larger scales, or increased complexity, or types of environments that we can’t physically gain access to—it really allows for us to find where some of those limits are in the technology based only on our imagination, and this is one of the intrinsic values of simulation.
But I think finding a way to incorporate human input, or more generally human factors like teleoperation interfaces and the in-situ stress that you might not be able to recreate in the context of a virtual competition provided a good reason for us to delineate the two competitions, with the Virtual Competition really being about the role of fully autonomous or self-sufficient systems going off and doing their solution without human guidance, while also acknowledging that the real world has conditions that would not necessarily be represented by a fully simulated version. Having said that, I think cognitive engineering still has an incredibly important role to play in human robot interaction.
What do we have to look forward to during the Virtual Competition Showcase?
We have a number of additional features and capabilities that we’ve baked into the simulator that will allow for us to derive some additional insights into our competition runs. Those insights might involve things like the performance of one or more robots in a given scenario, or the impact of the environment on different types of robots, and what I can tease is that this will be an opportunity for us to showcase both the technology and also the excitement of the robots competing in the virtual environment. I’m trying not to give too many spoilers, but we’ll have an opportunity to really get into the details.
Check back as we get closer to the 17 November event for more on the DARPA SubT Challenge. Continue reading
Posted in Human Robots
#437603 Throwable Robot Car Always Lands on Four ...
Throwable or droppable robots seem like a great idea for a bunch of applications, including exploration and search and rescue. But such robots do come with some constraints—namely, if you’re going to throw or drop a robot, you should be prepared for that robot to not land the way you want it to land. While we’ve seen some creative approaches to this problem, or more straightforward self-righting devices, usually you’re in for significant trade-offs in complexity, mobility, and mass.
What would be ideal is a robot that can be relied upon to just always land the right way up. A robotic cat, of sorts. And while we’ve seen this with a tail, for wheeled vehicles, it turns out that a tail isn’t necessary: All it takes is some wheel spin.
The reason that AGRO (Agile Ground RObot), developed at the U.S. Military Academy at West Point, can do this is because each of its wheels is both independently driven and steerable. The wheels are essentially reaction wheels, which are a pretty common way to generate forces on all kinds of different robots, but typically you see such reaction wheels kludged onto these robots as sort of an afterthought—using the existing wheels of a wheeled robot is a more elegant way to do it.
Four steerable wheels with in-hub motors provide control in all three axes (yaw, pitch, and roll). You’ll notice that when the robot is tossed, the wheels all toe inwards (or outwards, I guess) by 45 degrees, positioning them orthogonal to the body of the robot. The front left and rear right wheels are spun together, as are the front right and rear left wheels. When one pair of wheels spins in the same direction, the body of the robot twists in the opposite way along an axis between those wheels, in a combination of pitch and roll. By combining different twisting torques from both pairs of wheels, pitch and roll along each axis can be adjusted independently. When the same pair of wheels spin in directions opposite to each other, the robot yaws, although yaw can also be derived by adjusting the ratio between pitch authority and roll authority. And lastly, if you want to sacrifice pitch control for more roll control (or vice versa) the wheel toe-in angle can be changed. Put all this together, and you get an enormous amount of mid-air control over your robot.
Image: Robotics Research Center/West Point
The AGRO robot features four steerable wheels with in-hub motors, which provide control in all three axes (yaw, pitch, and roll).
According to a paper that the West Point group will present at the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), the overall objective here is for the robot to reach a state of zero pitch or roll by the time the robot impacts with the ground, to distribute the impact as much as possible. AGRO doesn’t yet have a suspension to make falling actually safe, so in the short term, it lands on a foam pad, but the mid-air adjustments it’s currently able to make result in a 20 percent reduction of impact force and a 100 percent reduction in being sideways or upside-down.
The toss that you see in the video isn’t the most aggressive, but lead author Daniel J. Gonzalez tells us that AGRO can do much better, theoretically stabilizing from an initial condition of 22.5 degrees pitch and 22.5 degrees roll in a mere 250 milliseconds, with room for improvement beyond that through optimizing the angles of individual wheels in real time. The limiting factor is really the amount of time that AGRO has between the point at which it’s released and the point at which it hits the ground, since more time in the air gives the robot more time to change its orientation.
Given enough height, the current generation of AGRO can recover from any initial orientation as long as it’s spinning at 66 rpm or less. And the only reason this is a limitation at all is because of the maximum rotation speed of the in-wheel hub motors, which can be boosted by increasing the battery voltage, as Gonzalez and his colleagues, Mark C. Lesak, Andres H. Rodriguez, Joseph A. Cymerman, and Christopher M. Korpela from the Robotics Research Center at West Point, describe in the IROS paper, “Dynamics and Aerial Attitude Control for Rapid Emergency Deployment of the Agile Ground Robot AGRO.”
Image: Robotics Research Center/West Point
AGRO 2 will include a new hybrid wheel-leg and non-pneumatic tire design that will allow it to hop up stairs and curbs.
While these particular experiments focus on a robot that’s being thrown, the concept is potentially effective (and useful) on any wheeled robot that’s likely to find itself in mid-air. You can imagine it improving the performance of robots doing all sorts of stunts, from driving off ramps or ledges to being dropped out of aircraft. And as it turns out, being able to self-stabilize during an airdrop is an important skill that some Humvees could use to keep themselves from getting tangled in their own parachute lines and avoid mishaps.
Before they move on to Humvees, though, the researchers are working on the next version of AGRO named AGRO 2. AGRO 2 will include a new hybrid wheel-leg and non-pneumatic tire design that will allow it to hop up stairs and curbs, which sounds like a lot of fun to us. Continue reading
Posted in Human Robots
#437585 Dart-Shooting Drone Attacks Trees for ...
We all know how robots are great at going to places where you can’t (or shouldn’t) send a human. We also know how robots are great at doing repetitive tasks. These characteristics have the potential to make robots ideal for setting up wireless sensor networks in hazardous environments—that is, they could deploy a whole bunch of self-contained sensor nodes that create a network that can monitor a very large area for a very long time.
When it comes to using drones to set up sensor networks, you’ve generally got two options: A drone that just drops sensors on the ground (easy but inaccurate and limited locations), or using a drone with some sort of manipulator on it to stick sensors in specific places (complicated and risky). A third option, under development by researchers at Imperial College London’s Aerial Robotics Lab, provides the accuracy of direct contact with the safety and ease of use of passive dropping by instead using the drone as a launching platform for laser-aimed, sensor-equipped darts.
These darts (which the researchers refer to as aerodynamically stabilized, spine-equipped sensor pods) can embed themselves in relatively soft targets from up to 4 meters away with an accuracy of about 10 centimeters after being fired from a spring-loaded launcher. They’re not quite as accurate as a drone with a manipulator, but it’s pretty good, and the drone can maintain a safe distance from the surface that it’s trying to add a sensor to. Obviously, the spine is only going to work on things like wood, but the researchers point out that there are plenty of attachment mechanisms that could be used, including magnets, adhesives, chemical bonding, or microspines.
Indoor tests using magnets showed the system to be quite reliable, but at close range (within a meter of the target) the darts sometimes bounced off rather than sticking. From between 1 and 4 meters away, the darts stuck between 90 and 100 percent of the time. Initial outdoor tests were also successful, although the system was under manual control. The researchers say that “regular and safe operations should be carried out autonomously,” which, yeah, you’d just have to deal with all of the extra sensing and hardware required to autonomously fly beneath the canopy of a forest. That’s happening next, as the researchers plan to add “vision state estimation and positioning, as well as a depth sensor” to avoid some trees and fire sensors into others.
And if all of that goes well, they’ll consider trying to get each drone to carry multiple darts. Look out, trees: You’re about to be pierced for science.
“Unmanned Aerial Sensor Placement for Cluttered Environments,” by André Farinha, Raphael Zufferey, Peter Zheng, Sophie F. Armanini, and Mirko Kovac from Imperial College London, was published in IEEE Robotics and Automation Letters.
< Back to IEEE Journal Watch Continue reading
Posted in Human Robots