Mechanical gliders can learn to soar like birds with the help of machine learning, reports a study published online this week in Nature.
Soaring birds ride warm, rising air currents - or ‘thermals’ - to fly and gain height without needing to flap their wings. However, the landscape of these currents is complex and continuously changing, and so exactly how birds find and navigate thermals is not understood. Without a full picture, it is hard to teach flying machines to do the same in the field.
To tackle this challenge, Massimo Vergassola and colleagues turn to so-called ‘reinforcement learning’. This is a dynamic machine learning technique in which an artificial agent learns by interacting with its environment, much like a child, and is ‘rewarded’ for correct behaviour and ‘penalized’ for poor choices. The authors programmed a two-metre-wingspan glider to adjust its aerial roll and pitch on the basis of on-board measurements of the surrounding environment. By pooling the glider’s collective experiences after a few days of test flights, a navigational strategy was devised, guided by the vertical wind acceleration and roll-wise torque (the force acting to rotate the glider side-to-side) as navigational cues. The authors suggest that the success of this strategy in the field indicates that birds might also rely on such cues.
The authors note that being able to soar along an individual thermal is just one part of how migrating birds - or their mechanical mimics - can safely undertake quick journeys across hundreds of kilometres. Future, supplementary research into the navigational cues that could help to identify strong updrafts will further enhance our understanding of how birds migrate and could aid in the development of efficient long-distance autonomous gliders.
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