优化燃料受限的无人机-无人地面车路径以实现大规模覆盖：异构多智能体系统中的双层规划
Optimizing Fuel-Constrained UAV-UGV Routes for Large Scale Coverage: Bilevel Planning in Heterogeneous Multi-Agent Systems
AI总结：
本文针对燃料受限的UAV-UGV异构多智能体系统大规模覆盖路径规划问题，提出多种启发式方法，结合最小集合覆盖、旅行商问题和带时间窗的车辆路径规划公式，实现可行且高效的协同路径求解。
摘要：
快速移动的无人机（UAV）非常适合空中监视，但受到电池容量的限制。为了增加其续航能力，无人机可以在缓慢移动的无人地面车（UGV）上补充燃料。在速度和燃料约束下，对UAV-UGV多智能体系统进行协同路径规划以勘测广阔区域是一个计算上具有挑战性的问题，但可以通过启发式方法加以简化。本文提出了多种启发式方法，以求得该问题的可行且足够优化的解。利用无人机燃料限制和最小集合覆盖算法，确定UGV的补给停靠点。这些补给停靠点使得任务点能够分配给无人机和无人地面车。采用标准旅行商问题公式以及带时间窗、舍弃访问和容量约束的车辆路径规划公式，分别求解UGV和UAV的路径。在小规模测试平台（http://tiny.cc/8or8vz）上的实验验证凸显了我们多智能体方法的有效性。
英文摘要：
Fast moving unmanned aerial vehicles (UAVs) are well suited for aerial surveillance, but are limited by their battery capacity. To increase their endurance UAVs can be refueled on slow moving unmanned ground vehicles (UGVs). The cooperative routing of UAV-UGV multi-agent system to survey vast regions within their speed and fuel constraints is a computationally challenging problem, but can be simplified with heuristics. Here we present multiple heuristics to enable feasible and sufficiently optimal solutions to the problem. Using the UAV fuel limits and the minimum set cover algorithm, the UGV refueling stops are determined. These refueling stops enable the allocation of mission points to the UAV and UGV. A standard traveling salesman formulation and a vehicle routing formulation with time windows, dropped visits, and capacity constraints is used to solve for the UGV and UAV route, respectively. Experimental validation on a small-scale testbed (http://tiny.cc/8or8vz) underscores the effectiveness of our multi-agent approach.
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