提出高效算法，让无人机与地面车协同充电，实现抗干扰的长时间空中监控。
RSPECT: Robust and Scalable Planner for Energy-Aware Coordination of UAV-UGV Teams in Aerial Monitoring
- 设计启发式算法RSPECT，解决无人机与地面车协同充电的鲁棒规划问题。
- 在仿真与实验中验证，算法能在不确定性下完成任务且耗时最少。
- 适合需要长时间、高可靠性空地协同监测的场景，如灾害救援或边境巡逻。
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（UGV）的鲁棒协同规划问题，旨在执行长周期空中监测任务。给定无人机需访问的若干目标点及团队期望的最终位置，目标是找到一条可在面对未知障碍物、地形或风力等不确定性时无需大幅修改的轨迹，以最短时间完成任务。我们将该问题形式化为一个混合整数规划（MIP），属于NP难问题。由于精确求解方法在计算上不可行，本文提出一种可扩展且高效的启发式算法RSPECT。我们提供了算法复杂度的理论分析，以及所生成计划在可行性与鲁棒性方面的证明。通过仿真与实际实验验证了方法的有效性。
原文摘要 · Abstract (English)
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.
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