Real-Time Learning of Wing Motion Correction in an Unconstrained Flapping-Wing Air Vehicle

Small Flapping-Wing Micro-Air Vehicles (FW-MAVs) can experience wing damage and wear while in service. Even small amounts of wing can prevent the vehicle from attaining desired waypoints without significant adaptation to onboard flight control. In previous work, we demonstrated that low-level adaptation of wing motion patterns, rather than high-level adaptation of path control, could restore acceptable performance. We further demonstrated that this low-level adaptation could be accomplished while the vehicle was in normal service and without requiring excessive amounts of flight time. Previous work, however, did not carefully consider the use of these methods when the vehicle was completely unconstrained in three-dimensional space (I.E. no mechanical safety supports) and when all vehicle degrees of freedom had to be simultaneously controlled. Also, previous work presumed that the learning algorithm could adapt wing motion patterns with minimal constraints on shape. The newest generation of FW-MAVs we consider place some significant constraints on legal wing motions which brings into question the efficacy of previous work for current vehicles. In this paper, we will provide compelling evidence that learning during unconstrained flight under the newly imposed wing motion conditions is both practical and feasible. This paper constitutes the first formal report of these results and removes the final barriers that had existed to implementation in a fully-realized physical FW-MAV.