Nonmyopic planning for long-term information gathering with an aerial glider

Thermal soaring can vastly increase the effectiveness of Unmanned Aerial Vehicles (UAVs) in information gathering tasks. However, knowing when to best collect information or regain energy is non-trivial. In this work, the problem is posed as a graph search problem where nodes are thermal positions, and edges are inter-thermal trajectories. Previous work has shown that this search problem is NP-hard, such that computing a long-duration plan is difficult without significant computational effort. This paper introduces two mechanisms to make this tractable. Firstly, Monte Carlo Tree Search (MCTS) is used to provide an anytime search strategy capable of generating long plans without exhaustive search. Secondly, a novel clustering approach isolates areas of interest on the information map to solve local cluster subproblems, followed by dynamic programming to optimally allocate search time to each cluster. Results demonstrate the improved performance of these approaches on longer missions.