Global planning method for UAVs based on pruned visibility map

To address the challenge of efficiently constructing environment maps and achieving long-distance global planning for UAVs in complex scenarios, this paper proposes a probabilistic update-based pruning visibility map construction method and a hierarchical planning strategy. The approach generates a grid map through probabilistic updates, extracts obstacle boundaries via hierarchical mapping, and constructs a visibility map with collision detection. A pruning strategy for the visibility map is introduced to reduce the search space and accelerate pathfinding. The hierarchical planning framework is based on search and optimization, where the outer planning layer employs an improved A* algorithm based on exploration degree. By incorporating path exploration degree into the cost function, global planning performance in complex environments is significantly enhanced. The inner planning layer uses trajectory optimization based on Minimum Control Effort (MINCO) trajectory representation to generate smooth flight paths that satisfy UAV speed and acceleration constraints. Experimental simulations and real-flight validations show that compared to the traditional A* algorithm, the proposed pruning visibility map-based improved A* algorithm reduces flight distance by 12.92% and flight time by 16.43%, demonstrating the algorithm's ability to improve planning efficiency and optimality in complex scenarios.