Fast moving but power hungry unmanned aerial vehicles (UAVs) can recharge on slow-moving unmanned ground vehicles (UGVs) to survey large areas in an effective and efficient manner. In order to solve this computationally challenging problem in a reasonable time, we created a two-level optimization heuristics. At the outer level, the UGV route is parameterized by few free parameters and at the inner level, the UAV route is solved by formulating and solving a vehicle routing problem with capacity constraints, time windows, and dropped visits. The UGV free parameters need to be optimized judiciously in order to create high quality solutions. We explore two methods for tuning the free UGV parameters: (1) a genetic algorithm, and (2) Asynchronous Multi-agent architecture (Ateams). The A-teams uses multiple agents to create, improve, and destroy solutions. The parallel asynchronous architecture enables A-teams to quickly optimize the parameters. Our results on test cases show that the A-teams produces similar solutions as genetic algorithm but with a speed up of 2-3 times.

翻译：快速移动但能耗较高的无人机可通过在低速移动的无人地面车辆上充电，以高效且有效的方式对大面积区域进行巡检。为在合理时间内解决这一计算挑战性问题，我们构建了一种双层优化启发式算法。外层将无人地面车辆路径通过少量自由参数进行参数化，内层则通过构建并求解带容量约束、时间窗及可跳过访问点的车辆路径问题来规划无人机路径。无人地面车辆的自由参数需要审慎优化以生成高质量解。我们探讨了两种调节无人地面车辆自由参数的方法：（1）遗传算法，（2）异步多智能体架构（A-teams）。A-teams 利用多个智能体协同完成解的生成、改进与淘汰。其并行异步架构使 A-teams 能够快速优化参数。测试案例结果表明，A-teams 能获得与遗传算法相近的解，但求解速度提升2-3倍。