This Autonomous Quadrotor Swarm Doesn't Need GPS

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"This Autonomous Quadrotor Swarm Doesn't Need GPS" in IEEE Spectrum

By Evan Ackerman
Posted 27 Dec 2017
 
The vast majority of the fancy autonomous flying we've seen from quadrotors has relied on some kind of external localization for position information. Usually it's a motion capture system, sometimes it's GPS, but either way, there's a little bit of cheating involved. This is not to say that we mind cheating, but the problem with cheating is that sometimes you can't cheat, and if you want your quadrotors to do tricks where you don't have access to GPS or the necessary motion capture hardware and software, you're out of luck.

Researchers are working hard towards independent autonomy for flying robots, and we've seen some impressive examples of drones that can follow paths and avoid obstacles using only onboard sensing and computing. The University of Pennsylvania has been doing some particularly amazing development in this area, and they've managed to teach a swarm of of a dozen 250g quadrotors to fly in close formation, even though each one is using just one small camera and a simple IMU. This is probably the largest swarm of quadrotors which don't rely on motion capture or GPS.

Each little quadrotor is equipped with a Qualcomm Snapdragon Flight development board. The board includes an onboard quad-core computer,  a downward facing VGA camera with 160◦ field of view, a VGA stereo camera pair, and a 4K video camera. For these flights, though, the drones are only using one or two cores of processing power (running ROS), a simple onboard IMU, and a downward-looking VGA camera with a 160 degree field of view.

Each quadrotor's job is to use visual inertial odometry (VIO) to estimate how far and in what direction it's moved from its starting position, which gives a good approximation of its relative location. To do this, it simply identifies and tracks visual features in its camera field of view: if the drone's camera sees an object, and that object moves right to left across the frame, the drone can infer (with some help from its IMU) that it's moving left to right. Either that, or there's an earthquake going on. Dead reckoning approaches like these do result in some amount of drift, where small errors in position estimation build up over time, but UPenn has managed to keep things under control, with overall positional errors of just over half a meter even after the drones have flown over 100 meters.

As each drone keeps track of its own position, it sends updates at 10 Hz over 5 GHz Wi-Fi to a ground station running ROS. The ground station collects all of those position updates, and sends commands back to the swarm to change formation. The only thing that the individual drones get back is a set of target coordinates and a time to start moving; each drone calculates is own trajectory, meaning that the ground station isn't doing all of the planning. This keeps things lightweight and distributed, so that the swarm can easily scale up to more drones. However, it's worth noting that as far as each drone is concerned, it's not really part of a swarm at all— it's just monitoring its own position and moving from coordinate to coordinate, and isn't aware (directly or indirectly) that there are other drones around it. Still, the system works very well, and as you can see from the video, the drones don't run into each other.

The outdoor testing that the video shows is notable for a few reasons. Some of it is done in very low light, which is always impressive to see for any VIO system, since it depends on identifying enough features to make a good position estimate, a tricky thing at night. And you can't tell from the video, but the average wind speed during the outdoor tests was 10mph, with 18mph gusts. It's a very robust tracking, in other words, which makes it more likely to be useful rather than just a novel demo. 

Read full article and interview with GRASP research scientist Giuseppe Loianno in the IEEE Spectrum.