Tag Archives: module

#439053 Bipedal Robots Are Learning To Move With ...

Posted on April 2, 2021 by Android

Most humans are bipeds, but even the best of us are really only bipeds until things get tricky. While our legs may be our primary mobility system, there are lots of situations in which we leverage our arms as well, either passively to keep balance or actively when we put out a hand to steady ourselves on a nearby object. And despite how unstable bipedal robots tend to be, using anything besides legs for mobility has been a challenge in both software and hardware, a significant limitation in highly unstructured environments.

Roboticists from TUM in Germany (with support from the German Research Foundation) have recently given their humanoid robot LOLA some major upgrades to make this kind of multi-contact locomotion possible. While it’s still in the early stages, it’s already some of the most human-like bipedal locomotion we’ve seen.

It’s certainly possible for bipedal robots to walk over challenging terrain without using limbs for support, but I’m sure you can think of lots of times where using your arms to assist with your own bipedal mobility was a requirement. It’s not a requirement because your leg strength or coordination or sense of balance is bad, necessarily. It’s just that sometimes, you might find yourself walking across something that’s highly unstable or in a situation where the consequences of a stumble are exceptionally high. And it may not even matter how much sensing you do beforehand, and how careful you are with your footstep planning: there are limits to how much you can know about your environment beforehand, and that can result in having a really bad time of it. This is why using multi-contact locomotion, whether it’s planned in advance or not, is a useful skill for humans, and should be for robots, too.

As the video notes (and props for being explicit up front about it), this isn’t yet fully autonomous behavior, with foot positions and arm contact points set by hand in advance. But it’s not much of a stretch to see how everything could be done autonomously, since one of the really hard parts (using multiple contact points to dynamically balance a moving robot) is being done onboard and in real time.

Getting LOLA to be able to do this required a major overhaul in hardware as well as software. And Philipp Seiwald, who works with LOLA at TUM, was able to tell us more about it.

IEEE Spectrum: Can you summarize the changes to LOLA’s hardware that are required for multi-contact locomotion?

Philipp Seiwald: The original version of LOLA has been designed for fast biped walking. Although it had two arms, they were not meant to get into contact with the environment but rather to compensate for the dynamic effects of the feet during fast walking. Also, the torso had a relatively simple design that was fine for its original purpose; however, it was not conceived to withstand the high loads coming from the hands during multi-contact maneuvers. Thus, we redesigned the complete upper body of LOLA from scratch. Starting from the pelvis, the strength and stiffness of the torso have been increased. We used the finite element method to optimize critical parts to obtain maximum strength at minimum weight. Moreover, we added additional degrees of freedom to the arms to increase the hands' reachable workspace. The kinematic topology of the arms, i.e., the arrangement of joints and link lengths, has been obtained from an optimization that takes typical multi-contact scenarios into account.

Why is this an important problem for bipedal humanoid robots?

Maintaining balance during locomotion can be considered the primary goal of legged robots. Naturally, this task is more challenging for bipeds when compared to robots with four or even more legs. Although current high-end prototypes show impressive progress, humanoid robots still do not have the robustness and versatility they need for most real-world applications. With our research, we try to contribute to this field and help to push the limits further. Recently, we showed our latest work on walking over uneven terrain without multi-contact support. Although the robustness is already high, there still exist scenarios, such as walking on loose objects, where the robot's stabilization fails when using only foot contacts. The use of additional hand-environment support during this (comparatively) fast walking allows a further significant increase in robustness, i.e., the robot's capability to compensate disturbances, modeling errors, or inaccurate sensor input. Besides stabilization on uneven terrain, multi-contact locomotion also enables more complex motions, e.g., stepping over a tall obstacle or toe-only contacts, as shown in our latest multi-contact video.

How can LOLA decide whether a surface is suitable for multi-contact locomotion?

LOLA’s visual perception system is currently developed by our project partners from the Chair for Computer Aided Medical Procedures & Augmented Reality at the TUM. This system relies on a novel semantic Simultaneous Localization and Mapping (SLAM) pipeline that can robustly extract the scene's semantic components (like floor, walls, and objects therein) by merging multiple observations from different viewpoints and by inferring therefrom the underlying scene graph. This provides a reliable estimate of which scene parts can be used to support the locomotion, based on the assumption that certain structural elements such as walls are fixed, while chairs, for example, are not.

Also, the team plans to develop a specific dataset with annotations further describing the attributes of the object (such as roughness of the surface or its softness) and that will be used to master multi-contact locomotion in even more complex scenes. As of today, the vision and navigation system is not finished yet; thus, in our latest video, we used pre-defined footholds and contact points for the hands. However, within our collaboration, we are working towards a fully integrated and autonomous system.

Is LOLA capable of both proactive and reactive multi-contact locomotion?

The software framework of LOLA has a hierarchical structure. On the highest level, the vision system generates an environment model and estimates the 6D-pose of the robot in the scene. The walking pattern generator then uses this information to plan a dynamically feasible future motion that will lead LOLA to a target position defined by the user. On a lower level, the stabilization module modifies this plan to compensate for model errors or any kind of disturbance and keep overall balance. So our approach currently focuses on proactive multi-contact locomotion. However, we also plan to work on a more reactive behavior such that additional hand support can also be triggered by an unexpected disturbance instead of being planned in advance.

What are some examples of unique capabilities that you are working towards with LOLA?

One of the main goals for the research with LOLA remains fast, autonomous, and robust locomotion on complex, uneven terrain. We aim to reach a walking speed similar to humans. Currently, LOLA can do multi-contact locomotion and cross uneven terrain at a speed of 1.8 km/h, which is comparably fast for a biped robot but still slow for a human. On flat ground, LOLA's high-end hardware allows it to walk at a relatively high maximum speed of 3.38 km/h.

Fully autonomous multi-contact locomotion for a life-sized humanoid robot is a tough task. As algorithms get more complex, computation time increases, which often results in offline motion planning methods. For LOLA, we restrict ourselves to gaited multi-contact locomotion, which means that we try to preserve the core characteristics of bipedal gait and use the arms only for assistance. This allows us to use simplified models of the robot which lead to very efficient algorithms running in real-time and fully onboard.

A long-term scientific goal with LOLA is to understand essential components and control policies of human walking. LOLA's leg kinematics is relatively similar to the human body. Together with scientists from kinesiology, we try to identify similarities and differences between observed human walking and LOLA’s “engineered” walking gait. We hope this research leads, on the one hand, to new ideas for the control of bipeds, and on the other hand, shows via experiments on bipeds if biomechanical models for the human gait are correctly understood. For a comparison of control policies on uneven terrain, LOLA must be able to walk at comparable speeds, which also motivates our research on fast and robust walking.

While it makes sense why the researchers are using LOLA’s arms primarily to assist with a conventional biped gait, looking ahead a bit it’s interesting to think about how robots that we typically consider to be bipeds could potentially leverage their limbs for mobility in decidedly non-human ways.

We’re used to legged robots being one particular morphology, I guess because associating them with either humans or dogs or whatever is just a comfortable way to do it, but there’s no particular reason why a robot with four limbs has to choose between being a quadruped and being a biped with arms, or some hybrid between the two, depending on what its task is. The research being done with LOLA could be a step in that direction, and maybe a hand on the wall in that direction, too. Continue reading →

Posted in Human Robots

#438014 Meet Blueswarm, a Smart School of ...

Posted on January 29, 2021 by Android

Anyone who’s seen an undersea nature documentary has marveled at the complex choreography that schooling fish display, a darting, synchronized ballet with a cast of thousands.

Those instinctive movements have inspired researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), and the Wyss Institute for Biologically Inspired Engineering. The results could improve the performance and dependability of not just underwater robots, but other vehicles that require decentralized locomotion and organization, such as self-driving cars and robotic space exploration.

The fish collective called Blueswarm was created by a team led by Radhika Nagpal, whose lab is a pioneer in self-organizing systems. The oddly adorable robots can sync their movements like biological fish, taking cues from their plastic-bodied neighbors with no external controls required. Nagpal told IEEE Spectrum that this marks a milestone, demonstrating complex 3D behaviors with implicit coordination in underwater robots.

“Insights from this research will help us develop future miniature underwater swarms that can perform environmental monitoring and search in visually-rich but fragile environments like coral reefs,” Nagpal said. “This research also paves a way to better understand fish schools, by synthetically recreating their behavior.”

The research is published in Science Robotics, with Florian Berlinger as first author. Berlinger said the “Bluedot” robots integrate a trio of blue LED lights, a lithium-polymer battery, a pair of cameras, a Raspberry Pi computer and four controllable fins within a 3D-printed hull. The fish-lens cameras detect LED’s of their fellow swimmers, and apply a custom algorithm to calculate distance, direction and heading.

Based on that simple production and detection of LED light, the team proved that Blueswarm could self-organize behaviors, including aggregation, dispersal and circle formation—basically, swimming in a clockwise synchronization. Researchers also simulated a successful search mission, an autonomous Finding Nemo. Using their dispersion algorithm, the robot school spread out until one could detect a red light in the tank. Its blue LEDs then flashed, triggering the aggregation algorithm to gather the school around it. Such a robot swarm might prove valuable in search-and-rescue missions at sea, covering miles of open water and reporting back to its mates.

“Each Bluebot implicitly reacts to its neighbors’ positions,” Berlinger said. The fish—RoboCod, perhaps?—also integrate a Wifi module to allow uploading new behaviors remotely. The lab’s previous efforts include a 1,000-strong army of “Kilobots,” and a robotic construction crew inspired by termites. Both projects operated in two-dimensional space. But a 3D environment like air or water posed a tougher challenge for sensing and movement.

In nature, Berlinger notes, there’s no scaly CEO to direct the school’s movements. Nor do fish communicate their intentions. Instead, so-called “implicit coordination” guides the school’s collective behavior, with individual members executing high-speed moves based on what they see their neighbors doing. That decentralized, autonomous organization has long fascinated scientists, including in robotics.

“In these situations, it really benefits you to have a highly autonomous robot swarm that is self-sufficient. By using implicit rules and 3D visual perception, we were able to create a system with a high degree of autonomy and flexibility underwater where things like GPS and WiFi are not accessible.”

Berlinger adds the research could one day translate to anything that requires decentralized robots, from self-driving cars and Amazon warehouse vehicles to exploration of faraway planets, where poor latency makes it impossible to transmit commands quickly. Today’s semi-autonomous cars face their own technical hurdles in reliably sensing and responding to their complex environments, including when foul weather obscures onboard sensors or road markers, or when they can’t fix position via GPS. An entire subset of autonomous-car research involves vehicle-to-vehicle (V2V) communications that could give cars a hive mind to guide individual or collective decisions— avoiding snarled traffic, driving safely in tight convoys, or taking group evasive action during a crash that’s beyond their sensory range.

“Once we have millions of cars on the road, there can’t be one computer orchestrating all the traffic, making decisions that work for all the cars,” Berlinger said.

The miniature robots could also work long hours in places that are inaccessible to humans and divers, or even large tethered robots. Nagpal said the synthetic swimmers could monitor and collect data on reefs or underwater infrastructure 24/7, and work into tiny places without disturbing fragile equipment or ecosystems.

“If we could be as good as fish in that environment, we could collect information and be non-invasive, in cluttered environments where everything is an obstacle,” Nagpal said. Continue reading →

Posted in Human Robots

#437697 These Underwater Drones Use Water ...

Posted on November 25, 2020 by Android

Yi Chao likes to describe himself as an “armchair oceanographer” because he got incredibly seasick the one time he spent a week aboard a ship. So it’s maybe not surprising that the former NASA scientist has a vision for promoting remote study of the ocean on a grand scale by enabling underwater drones to recharge on the go using his company’s energy-harvesting technology.

Many of the robotic gliders and floating sensor stations currently monitoring the world’s oceans are effectively treated as disposable devices because the research community has a limited number of both ships and funding to retrieve drones after they’ve accomplished their mission of beaming data back home. That’s not only a waste of money, but may also contribute to a growing assortment of abandoned lithium-ion batteries polluting the ocean with their leaking toxic materials—a decidedly unsustainable approach to studying the secrets of the underwater world.

“Our goal is to deploy our energy harvesting system to use renewable energy to power those robots,” says Chao, president and CEO of the startup Seatrec. “We're going to save one battery at a time, so hopefully we're going to not to dispose more toxic batteries in the ocean.”

Chao’s California-based startup claims that its SL1 Thermal Energy Harvesting System can already help save researchers money equivalent to an order of magnitude reduction in the cost of using robotic probes for oceanographic data collection. The startup is working on adapting its system to work with autonomous underwater gliders. And it has partnered with defense giant Northrop Grumman to develop an underwater recharging station for oceangoing drones that incorporates Northrop Grumman’s self-insulating electrical connector capable of operating while the powered electrical contacts are submerged.

Seatrec’s energy-harvesting system works by taking advantage of how certain substances transition from solid-to-liquid phase and liquid-to-gas phase when they heat up. The company’s technology harnesses the pressure changes that result from such phase changes in order to generate electricity.

Image: Seatrec

To make the phase changes happen, Seatrec’s solution taps the temperature differences between warmer water at the ocean surface and colder water at the ocean depths. Even a relatively simple robotic probe can generate additional electricity by changing its buoyancy to either float at the surface or sink down into the colder depths.

By attaching an external energy-harvesting module, Seatrec has already begun transforming robotic probes into assets that can be recharged and reused more affordably than sending out a ship each time to retrieve the probes. This renewable energy approach could keep such drones going almost indefinitely barring electrical or mechanical failures. “We just attach the backpack to the robots, we give them a cable providing power, and they go into the ocean,” Chao explains.

The early buyers of Seatrec’s products are primarily academic researchers who use underwater drones to collect oceanographic data. But the startup has also attracted military and government interest. It has already received small business innovation research contracts from both the U.S. Office of Naval Research and National Oceanic and Atmospheric Administration (NOAA).

Seatrec has also won two $10,000 prizes under the Powering the Blue Economy: Ocean Observing Prize administered by the U.S. Department of Energy and NOAA. The prizes awarded during the DISCOVER Competition phase back in March 2020 included one prize split with Northrop Grumman for the joint Mission Unlimited UUV Station concept. The startup and defense giant are currently looking for a robotics company to partner with for the DEVELOP Competition phase of the Ocean Observing Prize that will offer a total of $3 million in prizes.

In the long run, Seatrec hopes its energy-harvesting technology can support commercial ventures such as the aquaculture industry that operates vast underwater farms. The technology could also support underwater drones carrying out seabed surveys that pave the way for deep sea mining ventures, although those are not without controversy because of their projected environmental impacts.

Among all the possible applications Chao seems especially enthusiastic about the prospect of Seatrec’s renewable power technology enabling underwater drones and floaters to collect oceanographic data for much longer periods of time. He spent the better part of two decades working at the NASA Jet Propulsion Laboratory in Pasadena, Calif., where he helped develop a satellite designed for monitoring the Earth’s oceans. But he and the JPL engineering team that developed Seatrec’s core technology believe that swarms of underwater drones can provide a continuous monitoring network to truly begin understanding the oceans in depth.

The COVID-19 pandemic has slowed production and delivery of Seatrec’s products somewhat given local shutdowns and supply chain disruptions. Still, the startup has been able to continue operating in part because it’s considered to be a defense contractor that is operating an essential manufacturing facility. Seatrec’s engineers and other staff members are working in shifts to practice social distancing.

“Rather than building one or two for the government, we want to scale up to build thousands, hundreds of thousands, hopefully millions, so we can improve our understanding and provide that data to the community,” Chao says. Continue reading →

Posted in Human Robots

#437689 GITAI Sending Autonomous Robot to Space ...

Posted on November 24, 2020 by Android

We’ve been keeping a close watch on GITAI since early last year—what caught our interest initially is the history of the company, which includes a bunch of folks who started in the JSK Lab at the University of Tokyo, won the DARPA Robotics Challenge Trials as SCHAFT, got swallowed by Google, narrowly avoided being swallowed by SoftBank, and are now designing robots that can work in space.

The GITAI YouTube channel has kept us more to less up to date on their progress so far, and GITAI has recently announced the next step in this effort: The deployment of one of their robots on board the International Space Station in 2021.

Photo: GITAI

GITAI’s S1 is a task-specific 8-degrees-of-freedom arm with an integrated sensing and computing system and 1-meter reach.

GITAI has been working on a variety of robots for space operations, the most sophisticated of which is a humanoid torso called G1, which is controlled through an immersive telepresence system. What will be launching into space next year is a more task-specific system called the S1, which is an 8-degrees-of-freedom arm with an integrated sensing and computing system that can be wall-mounted and has a 1-meter reach.

The S1 will be living on board a commercially funded, pressurized airlock-extension module called Bishop, developed by NanoRacks. Mounted on the inside of the Bishop module, the S1 will have access to a task board and a small assembly area, where it will demonstrate common crew intra-vehicular activity, or IVA—tasks like flipping switches, turning knobs, and managing cables. It’ll also do some in-space assembly, or ISA, attaching panels to create a solar array.

Here’s a demonstration of some task board activities, conducted on Earth in a mockup of Bishop:

GITAI says that “all operations conducted by the S1 GITAI robotic arm will be autonomous, followed by some teleoperations from Nanoracks’ in-house mission control.” This is interesting, because from what we’ve seen until now, GITAI has had a heavy emphasis on telepresence, with a human in the loop to get stuff done. As GITAI’s founder and CEO Sho Nakanose commented to us a year ago, “Telepresence robots have far better performance and can be made practical much quicker than autonomous robots, so first we are working on making telepresence robots practical.”

So what’s changed? “GITAI has been concentrating on teleoperations to demonstrate the dexterity of our robot, but now it’s time to show our capabilities to do the same this time with autonomy,” Nakanose told us last week. “In an environment with minimum communication latency, it would be preferable to operate a robot more with teleoperations to enhance the capability of the robot, since with the current technology level of AI, what a robot can do autonomously is very limited. However, in an environment where the latency becomes noticeable, it would become more efficient to have a mixture of autonomy and teleoperations depending on the application. Eventually, in an ideal world, a robot will operate almost fully autonomously with minimum human cognizance.”

“In an environment where the latency becomes noticeable, it would become more efficient to have a mixture of autonomy and teleoperations depending on the application. Eventually, in an ideal world, a robot will operate almost fully autonomously with minimum human cognizance.”
—Sho Nakanose, GITAI founder and CEO

Nakanose says that this mission will help GITAI to “acquire the skills, know-how, and experience necessary to prepare a robot to be ISS compatible, prov[ing] the maturity of our technology in the microgravity environment.” Success would mean conducting both IVA and ISA experiments as planned (autonomous and teleop for IVA, fully autonomous for ISA), which would be pretty awesome, but we’re told that GITAI has already received a research and development order for space robots from a private space company, and Nakanose expects that “by the mid-2020s, we will be able to show GITAI's robots working in space on an actual mission.”

NanoRacks is schedule to launch the Bishop module on SpaceX CRS-21 in November. The S1 will be launched separately in 2021, and a NASA astronaut will install the robot and then leave it alone to let it start demonstrating how work in space can be made both safer and cheaper once the humans have gotten out of the way. Continue reading →

Posted in Human Robots

#437624 AI-Powered Drone Learns Extreme ...

Posted on November 22, 2020 by Android

Quadrotors are among the most agile and dynamic machines ever created. In the hands of a skilled human pilot, they can do some astonishing series of maneuvers. And while autonomous flying robots have been getting better at flying dynamically in real-world environments, they still haven’t demonstrated the same level of agility of manually piloted ones.

Now researchers from the Robotics and Perception Group at the University of Zurich and ETH Zurich, in collaboration with Intel, have developed a neural network training method that “enables an autonomous quadrotor to fly extreme acrobatic maneuvers with only onboard sensing and computation.” Extreme.

There are two notable things here: First, the quadrotor can do these extreme acrobatics outdoors without any kind of external camera or motion-tracking system to help it out (all sensing and computing is onboard). Second, all of the AI training is done in simulation, without the need for an additional simulation-to-real-world (what researchers call “sim-to-real”) transfer step. Usually, a sim-to-real transfer step means putting your quadrotor into one of those aforementioned external tracking systems, so that it doesn’t completely bork itself while trying to reconcile the differences between the simulated world and the real world, where, as the researchers wrote in a paper describing their system, “even tiny mistakes can result in catastrophic outcomes.”

To enable “zero-shot” sim-to-real transfer, the neural net training in simulation uses an expert controller that knows exactly what’s going on to teach a “student controller” that has much less perfect knowledge. That is, the simulated sensory input that the student ends up using as it learns to follow the expert has been abstracted to present the kind of imperfect, imprecise data it’s going to encounter in the real world. This can involve things like abstracting away the image part of the simulation until you’d have no way of telling the difference between abstracted simulation and abstracted reality, which is what allows the system to make that sim-to-real leap.

The simulation environment that the researchers used was Gazebo, slightly modified to better simulate quadrotor physics. Meanwhile, over in reality, a custom 1.5-kilogram quadrotor with a 4:1 thrust to weight ratio performed the physical experiments, using only a Nvidia Jetson TX2 computing board and an Intel RealSense T265, a dual fisheye camera module optimized for V-SLAM. To challenge the learning system, it was trained to perform three acrobatic maneuvers plus a combo of all of them:

Image: University of Zurich/ETH Zurich/Intel

Reference trajectories for acrobatic maneuvers. Top row, from left: Power Loop, Barrel Roll, and Matty Flip. Bottom row: Combo.

All of these maneuvers require high accelerations of up to 3 g’s and careful control, and the Matty Flip is particularly challenging, at least for humans, because the whole thing is done while the drone is flying backwards. Still, after just a few hours of training in simulation, the drone was totally real-world competent at these tricks, and could even extrapolate a little bit to perform maneuvers that it was not explicitly trained on, like doing multiple loops in a row. Where humans still have the advantage over drones is (as you might expect since we’re talking about robots) is quickly reacting to novel or unexpected situations. And when you’re doing this sort of thing outdoors, novel and unexpected situations are everywhere, from a gust of wind to a jealous bird.

For more details, we spoke with Antonio Loquercio from the University of Zurich’s Robotics and Perception Group.

IEEE Spectrum: Can you explain how the abstraction layer interfaces with the simulated sensors to enable effective sim-to-real transfer?

Antonio Loquercio: The abstraction layer applies a specific function to the raw sensor information. Exactly the same function is applied to the real and simulated sensors. The result of the function, which is “abstracted sensor measurements,” makes simulated and real observation of the same scene similar. For example, suppose we have a sequence of simulated and real images. We can very easily tell apart the real from the simulated ones given the difference in rendering. But if we apply the abstraction function of “feature tracks,” which are point correspondences in time, it becomes very difficult to tell which are the simulated and real feature tracks, since point correspondences are independent of the rendering. This applies for humans as well as for neural networks: Training policies on raw images gives low sim-to-real transfer (since images are too different between domains), while training on the abstracted images has high transfer abilities.

How useful is visual input from a camera like the Intel RealSense T265 for state estimation during such aggressive maneuvers? Would using an event camera substantially improve state estimation?

Our end-to-end controller does not require a state estimation module. It shares however some components with traditional state estimation pipelines, specifically the feature extractor and the inertial measurement unit (IMU) pre-processing and integration function. The input of the neural networks are feature tracks and integrated IMU measurements. When looking at images with low features (for example when the camera points to the sky), the neural net will mainly rely on IMU. When more features are available, the network uses to correct the accumulated drift from IMU. Overall, we noticed that for very short maneuvers IMU measurements were sufficient for the task. However, for longer ones, visual information was necessary to successfully address the IMU drift and complete the maneuver. Indeed, visual information reduces the odds of a crash by up to 30 percent in the longest maneuvers. We definitely think that event camera can improve even more the current approach since they could provide valuable visual information during high speed.

“The Matty Flip is probably one of the maneuvers that our approach can do very well … It is super challenging for humans, since they don’t see where they’re going and have problems in estimating their speed. For our approach the maneuver is no problem at all, since we can estimate forward velocities as well as backward velocities.”
—Antonio Loquercio, University of Zurich

You describe being able to train on “maneuvers that stretch the abilities of even expert human pilots.” What are some examples of acrobatics that your drones might be able to do that most human pilots would not be capable of?

The Matty Flip is probably one of the maneuvers that our approach can do very well, but human pilots find very challenging. It basically entails doing a high speed power loop by always looking backward. It is super challenging for humans, since they don’t see where they’re going and have problems in estimating their speed. For our approach the maneuver is no problem at all, since we can estimate forward velocities as well as backward velocities.

What are the limits to the performance of this system?

At the moment the main limitation is the maneuver duration. We never trained a controller that could perform maneuvers longer than 20 seconds. In the future, we plan to address this limitation and train general controllers which can fly in that agile way for significantly longer with relatively small drift. In this way, we could start being competitive against human pilots in drone racing competitions.

Can you talk about how the techniques developed here could be applied beyond drone acrobatics?

The current approach allows us to do acrobatics and agile flight in free space. We are now working to perform agile flight in cluttered environments, which requires a higher degree of understanding of the surrounding with respect to this project. Drone acrobatics is of course only an example application. We selected it because it makes a stress test of the controller performance. However, several other applications which require fast and agile flight can benefit from our approach. Examples are delivery (we want our Amazon packets always faster, don’t we?), search and rescue, or inspection. Going faster allows us to cover more space in less time, saving battery costs. Indeed, agile flight has very similar battery consumption of slow hovering for an autonomous drone.

“Deep Drone Acrobatics,” by Elia Kaufmann, Antonio Loquercio, René Ranftl, Matthias Müller, Vladlen Koltun, and Davide Scaramuzza from the Robotics and Perception Group at the University of Zurich and ETH Zurich, and Intel’s Intelligent Systems Lab, was presented at RSS 2020. Continue reading →

Posted in Human Robots