We investigate the problem of energy-constrained planning for a cooperative system of an Unmanned Ground Vehicles (UGV) and an Unmanned Aerial Vehicle (UAV). In scenarios where the UGV serves as a mobile base to ferry the UAV and as a charging station to recharge the UAV, we formulate a novel energy-constrained routing problem. To tackle this problem, we design an energy-aware routing algorithm, aiming to minimize the overall mission duration under the energy limitations of both vehicles. The algorithm first solves a Traveling Salesman Problem (TSP) to generate a guided tour. Then, it employs the Monte-Carlo Tree Search (MCTS) algorithm to refine the tour and generate paths for the two vehicles. We evaluate the performance of our algorithm through extensive simulations and a proof-of-concept experiment. The results show that our algorithm consistently achieves near-optimal mission time and maintains fast running time across a wide range of problem instances.

翻译：本研究探讨了无人地面车辆（UGV）与无人飞行器（UAV）协同系统中的能量受限规划问题。在UGV作为移动基站运送UAV并作为充电站为其补充能量的场景下，我们提出了一种新颖的能量受限路由问题。为解决此问题，我们设计了一种能量感知路由算法，旨在两种载具的能量限制下最小化整体任务时长。该算法首先求解旅行商问题（TSP）以生成引导路径，随后采用蒙特卡洛树搜索（MCTS）算法对路径进行优化，并为两种载具生成具体行驶轨迹。我们通过大量仿真和概念验证实验评估了算法的性能。结果表明，该算法在各类问题实例中均能持续实现接近最优的任务时间，并保持较快的运行速度。