In countries where commercial drone delivery is permitted beyond line-of-sight (hint: not the US), autonomous drones still have a big blind spot: Urban areas.
Due to interference from tall buildings, navigating city streets is all but impossible with GPS alone. Researchers at the University of Zurich and the National Centre of Competence in Research (NCCR Robotics) set out to tackle that problem.
Their solution? Design an algorithm that allows drones to autonomously navigate by mimicking the very traffic that delivery drones were invented to avoid.
The algorithm, called DroNet, uses input images captured with a drone’s cameras.
“DroNet recognizes static and dynamic obstacles and can slow down to avoid crashing into them,” explains Davide Scaramuzza, professor of Robotics and Perception at the University of Zurich. “With this algorithm we have taken a step forward towards integrating autonomously navigating drones into our everyday life.”
The use of cameras instead of higher-cost sensors was intentional, a way of ensuring that the algorithm can be used on a wide variety of platforms in production today. The smarts of the system come from the powerful artificial intelligence algorithm, which uses deep learning to interpret images and direct drones to react.