针对复杂城市环境下多无人机（UAVS）协同巡检、配送等任务，提出一种基于多指标动态优先级的协同路径规划方法，以节省运行成本和增加任务效率。综合考虑碰撞风险、总路程、等待时间等指标构建动态优先级模型，并在优先级单边避碰机制下，定制组合规避策略以处理局部冲突，更好地权衡协同规划效率和路径质量。针对无人机个体路径规划，在Lazy Theta*算法基础上引入拥堵权值地图，引导无人机避开拥堵区域，降低冲突发生可能性。对比仿真试验表明：提出的个体规划算法可以减少拥堵区域和降低拥堵持续时间，提出的多指标动态优先级协同规划算法相比于飞行时间驱动的动态优先级，能够提高规划效率和结果最优性。

To save operation costs and increase efficiency for collaborative inspection, distribution, and other tasks of multiple Unmanned Aerial Vehicles (UAVs) in complex urban environments, a cooperative path planning method is proposed based on multi-index dynamic priority. A dynamic priority model is constructed through a comprehensive consideration of the indices such as collision risk, total distance, and waiting time. Under the priority-based unilateral collision avoidance mechanism, a combined avoidance strategy is customized to handle local conflicts, so as to balance the planning efficiency and route quality. For individual path planning, a congestion weighted map is introduced into the Lazy Theta* algorithm to guide UAV to avoid congested areas and reduce the conflict possibility. Comparative simulation experiments show that the proposed individual planning algorithm can reduce congestion areas and congestion duration, and the proposed multi-index dynamic priority cooperative planning algorithm can improve planning efficiency and route optimality compared to the flight-time driven dynamic priority.