Using cameras and sensors, AI-controlled drones beat pilots at high speeds through obstacle-filled tracks.
AI-controlled drone races against human-controlled drone. Video: UZH
The Swift autonomous system beat three professional drone pilots in 15 of 25 races on a track full of curves and obstacles designed by a professional drone pilot, Science Alert reported on August 31. The system combines AI algorithms with a camera and many built-in sensors to detect the environment and the drone's movements.
Swift was designed by Elia Kaufmann, a robotics engineer at the University of Zurich, and researchers at Intel Labs. They aimed for a system that would not rely on input from external moving cameras like previous autonomous racing drones.
“Achieving professional pilot level performance with an autonomous drone is challenging because it needs to fly within its physical limits, while estimating speed and position on the track using only onboard sensors,” the team said.
Pilots wear special goggles that provide a “first-person” view (as if sitting inside the drone) through a camera mounted on the drone. The drone can reach speeds of 100 km/h.
Similarly, the Swift has a built-in camera and inertial sensors to measure the drone's acceleration and rotation. This data is analyzed by two AI algorithms to determine the drone's position relative to obstacles and issue corresponding control commands.
Despite losing 40% of the races, Swift beat the pilot several times and achieved the fastest recorded race time, half a second faster than the best human time.
“Overall, on average over the entire course, the autonomous drone achieved the highest average speed, found the shortest route, and successfully maintained its performance close to its limits throughout the race,” Kaufmann and his colleagues said.
The real innovation in Swift, says Guido de Croon, a roboticist at Delft University of Technology in the Netherlands, is the second neural network it deploys, using deep reinforcement learning. Swift isn’t the first drone system to fly around obstacles, but it does so with remarkable accuracy. The new research is published in the journal Nature.
Thu Thao (According to Science Alert )
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