Watch a Drone Swarm Fly Through a Fake Forest Without Crashing

Sorias team tested the new approach against a state-of-the-art reactive model on a simulation with five drones and eight obstacles, and confirmed their hunch. In one scenario, reactive swarms finished their mission in 34.1 secondsthe predictive one finished in 21.5.

Next came the real demonstration. Sorias team gathered small Crazyflie quadcopters used by researchers. Each one was tiny enough to fit in the palm of her hand and weighed less than a golf ball, but carried an accelerometer, a gyroscope, a pressure sensor, a radio transmitter, and small motion-capture balls, spaced a couple of inches apart and between the four blades. Readings from the sensors and the rooms motion-capture camera, which tracked the balls, flowed to a computer running each drones model as a ground control station. (The small drones cant carry the hardware needed to run predictive control computations onboard.)

Soria placed the drones on the floor in a start region near the first tree-like obstacles. As she launched the experiment, five drones sprang up and quickly moved to random positions in the 3D space above the takeoff area. Then the copters started moving. They slipped through the air, between the soft green obstacles, over, under, and around each other, and toward the finish line where they landed with a gentle bounce. No collisions. Just smooth uneventful swarming made possible by a barrage of mathematical computations updating in real time.

Video: Jamani Caillet/2021 EPFL

The results of the NMPC [nonlinear model predictive control] model are quite promising, writes Gbor Vsrhelyi, a roboticist at Etvs Lornd University in Budapest, Hungary, in an email to WIRED. (Vsrhelyis team created the reactive model Soria used, but he was not involved in the work.)

However, Vsrhelyi notes, the study doesnt address a crucial barrier to implementing predictive control: the computation requires a central computer. Outsourcing controls over long distances could leave the entire swarm susceptible to communication delays or errors. Simpler decentralized control systems may not find the best possible flight trajectory, but they can run on very small onboard devices (such as mosquitoes, lady bugs or small drones) and scale much, much better with swarm size, he writes. Artificialand naturaldrone swarms cant have bulky onboard computers.

It is a bit of a question of quality or quantity, Vsrhelyi continues. However, nature kind of has it both.

That’s where I say Yes, I can, says Dan Bliss, a systems engineer at Arizona State University. Bliss, who is not involved with Sorias team, leads a Darpa project to make mobile processing more efficient for drones and consumer tech. Even small drones are expected to become more computationally powerful with time. I take a couple-hundred-watt computer problem and try to put it on a processor that consumes 1 watt, he says. Bliss adds that creating an autonomous drone swarm isnt just a control problem, its also a sensing problem. Onboard tools that map the surrounding world, such as computer vision, require a lot of processing power.

Lately, Sorias team has been working on distributing the intelligence among the drones to accommodate larger swarms, and to handle dynamic obstacles. Prediction-minded drone swarms are, like burrito-delivery drones, many years away. But thats not never. Roboticists can see them in their futureand, most likely, in their neighbors too.

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