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.

翻译：快速移动的无人机（UAV）适用于空中监视任务，但其续航能力受限于电池容量。为提升续航能力，无人机可在缓慢移动的无人地面车辆（UGV）上进行燃料补给。在速度与燃料约束下，协调无人机-无人车多智能体系统完成大面积区域巡视是一个计算难题，但可通过启发式方法简化。本文提出多种启发式策略，以找到可行且充分优化的解决方案。基于无人机燃料限制与最小集合覆盖算法，确定UGV的燃料补给站点，这些站点进一步支持任务点在无人机与无人车之间的分配。分别采用标准旅行商问题模型和带时间窗、跳过访问及容量约束的车辆路径问题模型求解无人车与无人机的路径。在小型实验平台（http://tiny.cc/8or8vz）上的验证结果表明了所提多智能体方法的有效性。