This paper tackles strategic path planning for a multi-UAV routing problem in low altitude urban environment, where GNSS coverage challenges typically affect navigation performance and thus autonomous flight capabilities. These issues are addressed with a multi-step strategy that includes automated definition of GNSS-challenging volumes based on a georeferenced three-dimensional environment model, derivation of candidate obstacle-free paths between waypoints, waypoint assignment and definition of time-tagged trajectories for all UAVs. In all the steps, attention is paid to limiting the computational burden, thus ensuring applicability in real mission scenarios. Going beyond binary logics which consider GNSS-challenging volumes as obstacles, a discrete number of dilution of precision levels is considered, leading to different three-dimensional maps of the environment defined as “DOP layers”. Then, the basic idea to ensure that the designed trajectories are “flyable” with given positioning accuracy requirements is to take navigation performance into account at waypoint assignment level, using propagated covariance as a metric. The approach thus combines “navigation aware” planning with multi-vehicle task assignment, generating a solution that depends on the sensors embarked onboard the UAVs, and can be naturally extended to account for multi-sensor-based navigation architectures and ground-infrastructure support. The algorithm is tested in simulations based on a real world scenario, considering a wide combination of input parameters in terms of positioning error threshold, maximum UAV velocity, number of UAVs, navigation sensors performance, and mission epoch.

本文针对低海拔城市环境中的多无人机航路问题解决战略路径规划，在该环境中，GNSS覆盖挑战通常会影响导航性能，从而影响自动飞行能力。这些问题可通过多步骤策略解决，包括基于地理参考的三维环境模型自动定义具有挑战性的GNSS体积，推导路点之间的候选无障碍路径，路点分配以及为所有时间标记轨迹的定义无人机。在所有步骤中，都应注意限制计算负担，从而确保在实际任务场景中的适用性。除了将挑战GNSS的体积视为障碍的二进制逻辑之外，还考虑了离散量的精度水平稀释，导致定义为“ DOP层”的环境的不同三维地图。然后，在给定的定位精度要求下，确保设计的轨迹“可飞行”的基本思想是使用传播协方差作为度量标准，在航路点分配级别考虑导航性能。因此，该方法将“导航感知”计划与多车辆任务分配相结合，生成了一种解决方案，该解决方案取决于搭载在无人机上的传感器，并且可以自然扩展以解决基于多传感器的导航架构和地面基础设施的支持。该算法已在基于实际场景的仿真中进行了测试，并考虑了输入参数的广泛组合，包括定位误差阈值，最大无人机速度，无人机数量，导航传感器性能，