Soria’s 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 seconds—the predictive one finished in 21.5.
Next came the real demonstration. Soria’s 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 room’s motion-capture camera, which tracked the balls, flowed to a computer running each drone’s model as a ground control station. (The small drones can’t 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.
“The results of the NMPC [nonlinear model predictive control] model are quite promising,” writes Gábor Vásárhelyi, a roboticist at Eötvös Loránd University in Budapest, Hungary, in an email to WIRED. (Vásárhelyi’s team created the reactive model Soria used, but he was not involved in the work.)
However, Vásárhelyi notes, the study doesn’t 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. Artificial—and natural—drone swarms can’t have bulky onboard computers.
“It is a bit of a question of quality or quantity,” Vásárhelyi 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 Soria’s 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 isn’t just a control problem, it’s also a sensing problem. Onboard tools that map the surrounding world, such as computer vision, require a lot of processing power.
Lately, Soria’s 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 that’s not never. Roboticists can see them in their future—and, most likely, in their neighbor’s too.
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