Energy-constrained motion planning for information gathering with autonomous aerial soaring

Autonomous aerial soaring presents a unique opportunity to extend the flight duration of Unmanned Aerial Vehicles (UAVs). In this paper, we examine the problem of a gliding UAV searching for a ground target while simultaneously collecting energy from known thermal energy sources. The problem is posed as a tree search problem by noting that a long-duration mission can be divided into similar segments of flying between and climbing in thermals. The algorithm attempts to maximise the probability of detecting a target by exploring a tree of the possible thermal-to-thermal transitions to a fixed search depth and executing the highest utility plan. The sensitivity of the algorithm to different search depths is explored, and the method is compared against a locally-optimal myopic search algorithm. In larger, more complicated problems, the suggested method outperforms myopic search by sacrificing short-term utility to reach more valuable exploration areas later in the mission.