Unmanned Aerial Vehicles (UAVs), although adept at aerial surveillance, are often constrained by limited battery capacity. By refueling on slow-moving Unmanned Ground Vehicles (UGVs), their operational endurance can be significantly enhanced. This paper explores the computationally complex problem of cooperative UAV-UGV routing for vast area surveillance within the speed and fuel constraints, presenting a sequential multi-agent planning framework for achieving feasible and optimally satisfactory solutions. By considering the UAV fuel limits and utilizing a minimum set cover algorithm, we determine UGV refueling stops, which in turn facilitate UGV route planning at the first step and through a task allocation technique and energy constrained vehicle routing problem modeling with time windows (E-VRPTW) we achieve the UAV route at the second step of the framework. The effectiveness of our multi-agent strategy is demonstrated through the implementation on 30 different task scenarios across 3 different scales. This work offers significant insight into the collaborative advantages of UAV-UGV systems and introduces heuristic approaches to bypass computational challenges and swiftly reach high-quality solutions.

翻译：无人机虽擅长空中监视，但其电池容量有限常构成制约因素。通过在低速移动的无人地面车辆上进行燃料补给，无人机的持续作业能力可得到显著增强。本文探讨了在速度和燃料约束下，面向广域监视任务的无人机-无人车协同路由这一计算复杂性问题，并提出了一种序贯多智能体规划框架，以实现可行且理论最优的满意解。通过考虑无人机的燃料限制并利用最小集合覆盖算法，我们首先确定无人地面车辆的燃料补给点，进而规划其行驶路径；在框架的第二步中，通过任务分配技术以及带时间窗的能源受限车辆路径问题建模，实现无人机的路径规划。通过在3种不同规模下的30个任务场景中进行仿真验证，我们证明了该多智能体策略的有效性。本研究为无人机-无人车系统的协同优势提供了重要见解，并引入了启发式方法，以规避计算挑战并快速获得高质量解。