关键词: 多无人机系统, 协同路径规划, 多指标动态优先级, Lazy Theta*算法, 拥堵权值地图, 组合规避策略

Abstract:

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.

Key words: multi-UAV system, cooperative path planning, multi-index dynamic priority, Lazy Theta* algorithm, congestion weighted map, combined avoidance strategy