Flocking with fixed-wing UAVs for distributed sensing: A stochastic optimal control approach

This work focuses on enabling multiple UAVs to flock together in order to distribute and collectively perform a given sensing task. Flocking is performed in a leader-follower fashion, and the leader is assumed to already have an effective control policy for the particular task. The UAVs are small fixed-wing aircraft cruising at a constant speed and fixed altitude, but experience stochasticity in their dynamics. Accordingly, the control problem for each follower is addressed in the context of stochastic optimal control, wherein the cost is a function of distance and heading with respect to the leader. The problem is solved offline via dynamic programming to minimize the expected cost over a finite horizon and generate a receding horizon optimal control policy. This flocking algorithm was successfully applied in the field, where three camera-equipped UAVs flocked together to perform vision-based target tracking. The experimental results verify the efficacy of the approach and show the benefits of flocking with multiple UAVs to distribute sensing tasks, which include a dramatic reduction in overall sensing error and robustness to individual sensor faults.