燃料受限的UAV-UGV路径问题的协作多智能体规划框架
Cooperative Multi-Agent Planning Framework for Fuel Constrained UAV-UGV Routing Problem
AI总结：
本文针对燃料受限的UAV-UGV协作路径问题，提出一种两阶段序列多智能体规划框架，结合最小集合覆盖算法和E-VRPTW建模，快速生成高质量解决方案，并在30个场景中验证了其有效性。
摘要：
无人机（UAV）虽然擅长空中监视，但常受限于有限的电池容量。通过在慢速移动的无人地面车辆（UGV）上补充燃料，其运行续航能力可以得到显著提升。本文探讨了在速度和燃料约束下，用于广阔区域监视的UAV-UGV协作路径这一计算复杂问题，提出了一种序列多智能体规划框架以获得可行且令人满意的最优解。通过考虑UAV燃料限制并利用最小集合覆盖算法，我们确定了UGV的补给停靠点，这进而在框架的第一步促进了UGV路径规划；通过任务分配技术和带时间窗的能量受限车辆路径问题建模（E-VRPTW），我们在框架的第二步实现了UAV路径规划。通过在3种不同规模的30个不同任务场景中实施，我们证明了该多智能体策略的有效性。这项工作为UAV-UGV系统的协作优势提供了深刻见解，并引入了启发式方法以绕过计算挑战，快速达到高质量解决方案。
英文摘要：
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.
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