Learning in the field how to soar like a bird
Abstract:
Soaring birds often rely on ascending thermal plumes in the atmosphere as they search for prey or migrate across large distances. The landscape of convective currents is turbulent and rapidly shifts on timescales of a few minutes as thermals constantly form, disintegrate, or are transported away by the wind. How soaring birds find and navigate thermals within this complex landscape is unknown. Reinforcement learning can be used to find an effective navigational strategy as a sequence of decisions taken in response to environmental cues. Reinforcement learning was applied to train gliders in the field to autonomously navigate atmospheric thermals. Gliders of two-meter wingspan were equipped with a flight controller that enabled an on-board implementation of autonomous flight policies via precise control over their bank angle and pitch. Learning is severely challenged by a multitude of physical effects and the unpredictability of the natural environment. A navigational strategy was determined solely from the experiences collected over several days in the field using exploratory behavioral policies. Bird-like performance was achieved and several viable biological mechanosensory cues were identified for soaring birds, which are also directly applicable to the development of autonomous soaring vehicles.