In a study published Oct. 14 in the journal Science Robotics, scientists created an AI system capable of pushing a quadcopter through a demanding course at over 60 mph (100 km/h), using little more than a single forward-facing camera, an inertial measurement unit, and a tiny onboard computer.
MonoRace accomplished this without the aid of GPS, external motion-capture systems, stereo cameras, or a preloaded map of the course. The system is an onboard autonomous flight model created by researchers at the Delft University of Technology's Micro Air Vehicles Lab in the Netherlands. Its name reflects its central design feature; "mono" refers to its monocular vision. The drone relies on a single rolling shutter camera and monitors its own motion with an internal measurement unit (IMU) that gauges acceleration and rotation.
The system fits a drone that weighs a mere 966 grams (2.13 pounds) and measures approximately 11.8 x 11 x 3.9 inches (30 x 28 x 10 centimeters). Because the MonoRace hardware was so compact, lightweight and computationally efficient, the team was able to operate it entirely aboard the drone.
A recently released video tracks MonoRace's development and performance in the competition, with real flight sequences demonstrating how the drone banks sharply, accelerates through gates, and navigates a course without human assistance.
"Here we show the gates in a visual simulator for the video but the real training happened in a stripped-down, pure physics simulator that ran really fast," de Croon told Live Science in an email. "Simulating vision was not done during training, as the control neural network receives an estimated state as input, which comes from a different perception and state estimation process."
How the AI learned to race
Scientists trained the MonoRace model with reinforcement learning, a machine learning method in which an agent repeatedly attempts a task and receives feedback based on its success or failure.
The network learned to translate the drone's position on the track, velocity and orientation directly into motor commands. According to de Croon, early training focused on the basics, like avoiding crashes and reaching the first gate. As the training progressed, the system learned to advance farther along the course and fly at higher speeds.
"That ensures that the trained network can control different drones," de Croon said.
Related storiesMonoRace could become a training tool for human drone pilots, "a bit like powerful chess programs have also inspired humans playing the game," de Croon said.
De Croon noted that a similarly sized network could optimize for minimal energy use rather than minimum lap time. Because most machines rely on small processors and conventional controllers, energy-optimal neural control could eventually reduce power consumption in everyday robotics, he said.
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