Even with all the technological advancements in recent years, autonomous systems have never been able to keep up with top-level human racing drone pilots. However, it looks like that gap has been closed with Swift – an autonomous system developed by the University of Zurich’s Robotics and Perception Group.
Previous research projects have come close, but they relied on optical motion capture settings in a tightly controlled environment. In contrast, Swift is completely independent of remote inputs and utilizes only an onboard computer, IMU, and camera for real-time for navigation and control. It does however require a pretrained machine learning model for the specific track, which maps the drone’s estimated position/velocity/orientation directly to control inputs. The details of how the system works is well explained in the video after the break.
The paper linked above contains a few more interesting details. Swift was able to win 60% of the time, and it’s lap times were significantly more consistent than those of the human pilots. While human pilots were often faster on certain sections of the course, Swift was faster overall. It picked more efficient trajectories over multiple gates, where the human pilots seemed to plan one gate in advance at most. On the other hand human pilots could recover quickly from a minor crash, where Swift did not include crash recovery.
The final results are impressive, especially given that all the processing and sensing comes from the drone. However, it still requires a well mapped track, so a human pilot should still come out on top given limited information about a new track. It would also be interesting to see how it handles large courses with gates that are much further apart.
Continue reading “Autonomous Racing Drones Are Starting To Beat Human Pilots”
If you’re [Nick Rehm], you want a drone that can plan its own routes even at low altitudes with unplanned obstacles blocking its way. (Video, embedded below.) And or course, you build it from scratch.
Why? Getting a drone that can fly a path and even return home when the battery is low, signal is lost, or on command, is simple enough. Just go to your favorite retailer, search “gps drone” and you can get away for a shockingly low dollar amount. This is possible because GPS receivers have become cheap, small, light, and power efficient. While all of these inexpensive drones can fly a predetermined path, they usually do so by flying over any obstacles rather than around.
[Nick Rehm] has envisioned a quadcopter that can do all of the things a GPS-enabled drone can do, without the use of a GPS receiver. [Nick] makes this possible by using algorithms similar to those used by Google Maps, with data coming from a typical IMU, a camera for Computer Vision, LIDAR for altitude, and an Intel RealSense camera for detection of position and movement. A Raspberry Pi 4 running Robot Operating System runs the autonomous show, and a Teensy takes care of flight control duties.
What we really enjoy about [Nick]’s video is his clear presentation of complex technologies, and a great sense of humor about a project that has consumed untold amounts of time, patience, and duct tape.
We can’t help but wonder if DARPA will allow [Nick] to fly his drone in the Subterranean Challenge such as the one hosted in an unfinished nuclear power plant in 2020.
Continue reading “Autonomous Drone Dodges Obstacles Without GPS”
When it was first announced that limits would be placed on recreational RC aircraft heavier than 250 grams, many assumed the new rules meant an end to home built quadcopters. But manufacturers rose to the challenge, and started developing incredibly small and lightweight versions of their hardware. Today, building and flying ultra-lightweight quadcopters with first person view (FPV) cameras has become a dedicated hobby onto itself.
But as impressive as those featherweight flyers might be, the CogniFly Project is really pushing what we thought was possible in this weight class. Designed as a platform for experimenting with artificially intelligent drones, this open source quadcopter is packing a Raspberry Pi Zero and Google’s AIY Vision Kit so it can perform computationally complex tasks such as image recognition while airborne. In case any of those experiments take an unexpected turn, it’s also been enclosed in a unique flexible frame that makes it exceptionally resilient to crash damage. As you can see in the video after the break, even after flying directly into a wall, the CogniFly can continue on its way as if nothing ever happened.
Continue reading “Resilient AI Drone Packs It All In Under 250 Grams”
It’s amazing how many things have managed to move online in recent weeks, many with a beneficial side effect of eliminating travel making them more accessible to everyone around the world. Though some events had a virtual track before it was cool, among them the DARPA Subterranean Challenge (SubT) robotics competition. Recent additions to their “Hello World” tutorials (with promise of more to come) have continued to lower the barrier of entry for aspiring roboticists.
We all love watching physical robots explore the real world, which is why SubT’s “Systems Track” gets most of the attention. But such participation is necessarily restricted to people who have the resources to build and transport bulky hardware to the competition site, which is just a tiny subset of all the brilliant minds who can contribute. Hence the “Virtual Track” which is accessible to anyone with a computer that meets requirements. (64-bit Ubuntu 18 with NVIDIA GPU) The tutorials help get us up and running on SubT’s virtual testbed which continues to evolve. With every round, the organizers work to bring the virtual and physical worlds closer together. During the recent Urban Circuit, they made high resolution scans of both the competition course as well as participating robots.
There’s a lot of other traffic on various SubT code repositories. Motivated by Bitbucket sunsetting their Mercurial support, SubT is moving from Bitbucket to GitHub and picking up some housecleaning along the way. Together with the newly added tutorials, this is a great time to dive in and see if you want to assemble a team (both of human collaborators and virtual robots) to join in the next round of virtual SubT. But if you prefer to stay an observer of the physical world, enjoy this writeup with many fun details on systems track robots.
Drones have come a long way in the past decade, and a lot of the pioneering work that made them mainstream was done by individual hackers and small teams. This often involves cobbling together components into flying crow’s nests of wiring. To streamline things a bit for hackers, the team at Luminous Bees are working on Ardubee, a small 3″ drone designed from the ground up for hacking.
The Ardubee is built around a single PCB that also acts as the frame of the drone. Onboard is an STM32F427 microcontroller, IMU, barometer and compass, ESCs, ESP8266 for telemetry, and a downward-facing range finder. It’s ready to connect to an SBUS RC receiver and a range of pluggable modules are in development to expand the drone’s capabilities. It’s designed to run the open-source Ardupilot software, which we’ve seen in so many DIY autonomous vehicles. Power is provided by a single 18650, which will probably limit higher speed maneuverability a bit but should be fine for the slower precision flight that such a drone is likely to be used for.
The team already has a swarm of larger 5″ drones that they developed for light shows. In the process they developed their own Ultrawide-band indoor positioning system, which will also be available for the Ardubee. They hope to launch a Kickstarter campaign soon and are asking for input from the community, so they can know what features need to be prioritized. We look forward to seeing where this project goes!
Autonomous vehicles are a popular topic around here for air, land, and water, and we have no doubt there will be many more.
Thanks for the tip [Andreas]!
Robots might be finding their footing above ground, but today’s autonomous robots have a difficult time operating underground. DARPA wanted to give the state of the art a push forward, so they are running a Subterranean (SubT) Challenge which just wrapped up its latest round. A great review of this Urban Circuit competition (and some of the teams participating in it) has been published by IEEE Spectrum. This is the second of three underground problem subdomains presented to the participants, six months apart, preparing them for the final event which will combine all three types.
If you missed the livestream or prefer edited highlight videos, they’re all part of DARPAtv’s Subterranean Challenge playlist. Today it starts with a compilation of Urban Circuit highlights and continues to other videos. Including team profiles, video walkthrough of competition courses, actual competition footage, edited recap videos, and the awards ceremony. Half of the playlist are video from the Tunnels Circuit six months ago, so we can compare to see how teams performed and what they’ve learned along the way. Many more lessons were learned in the just-completed Urban Circuit and teams will spend the next six months improving their robots. By then we’ll have the Caves Circuit competition with teams ready to learn new lessons about operating robots underground.
Continue reading “DARPA Challenge Autonomous Robot Teams To Navigate Unfinished Nuclear Power Plant”
Currently underway is the DARPA Subterranean Challenge (SubT) systems competition for urban circuits streamed live on YouTube now through Wednesday, February 26th.
The DARPA Grand Challenge of 2004 kicked research and development of autonomous vehicles into high gear. Many components on today’s self-driving vehicles can be traced back to systems developed for that competition. Hoping to spur further development, DARPA has since held several more challenges focused moving the state of the art in autonomous robotics ahead.
To succeed in this challenge, robots must handle terrain that would confuse today’s self-driving cars. Cluttered environments, uneven surfaces of different materials, even the occasional flooded section are fair game. These robots also lose access to some of the tools previously available, such as GPS. The “systems track” denotes teams building physical robot systems versus a separate “virtual track” for simulation robots. “Urban circuit” is the second of four phases in this competition, environments of this phase are focused on man-made underground structures. (Think subway station.) For more details on this competition as well as description of various phases, see our introductory post or the competition site.
Those who rather not watch robots tentatively exploring unknown territory (and occasionally failing) may choose to wait for summaries published after competition rounds are complete. The first phase (tunnel circuit) from August-October 2019 was summarized by IEEE Spectrum here. Or you can go straight to DARPA for details on the systems track and virtual track with overall results posted on the competition site.
Continue reading “DARPA Subterranean Challenge Urban Circuit Now Livestreaming”