We consider the robust planning of energy-constrained unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), which act as mobile charging stations, to perform long-horizon aerial monitoring missions. More specifically, given a set of points to be visited by the UAVs and desired final positions of the UAV-UGV teams, the objective is to find a robust plan (the vehicle trajectories) that can be realized without a major revision in the face of uncertainty (e.g., unknown obstacles/terrain, wind) to complete this mission in minimum time. We provide a formal description of this problem as a mixed-integer program (MIP), which is NP-hard. Since exact solution methods are computationally intractable for such problems, we propose RSPECT, a scalable and efficient heuristic. We provide theoretical results on the complexity of our algorithm and the feasibility and robustness of resulting plans. We also demonstrate the performance of our method via simulations and experiments.

翻译：本文研究能量受限的无人机（UAV）与作为移动充电站的无人地面车辆（UGV）在长周期空中监测任务中的鲁棒规划问题。具体而言，给定一组需要无人机访问的目标点及无人机-无人车团队的期望最终位置，目标是在存在不确定性因素（如未知障碍物/地形、风力）的情况下，找到一个无需重大调整即可实现的鲁棒规划方案（即车辆轨迹），并以最短时间完成该任务。我们将该问题形式化描述为一个混合整数规划（MIP）问题，该问题属于NP难问题。由于此类问题的精确求解方法在计算上难以处理，我们提出了可扩展且高效的启发式算法RSPECT。本文给出了算法复杂度的理论结果，以及所生成规划方案的可行性与鲁棒性分析。最后通过仿真与实验验证了该方法的性能。