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 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 of the approach on a small-scale testbed shows the efficacy of the approach.

翻译：快速移动的无人机（UAVs）非常适合于空中监视，但其续航能力受限于电池容量。为了提升其续航能力，无人机可以在缓慢移动的无人地面车辆（UGVs）上进行燃料补给。在速度和燃料约束下，无人机-无人车协同巡查广阔区域的路径规划问题在计算上具有挑战性，但可以通过启发式方法简化。本文提出了多种启发式方法，以生成可行且足够优的解决方案。利用无人机的燃料限制与最小集合覆盖算法，确定了无人地面车辆的燃料补给站点。这些补给站点使得任务点能够在无人机和无人车之间进行分配。分别采用标准旅行商问题公式和带时间窗、访问跳过及容量约束的车辆路径问题公式，对无人车和无人机的路径进行求解。在小规模实验平台上的实验验证结果表明了该方法的有效性。