针对当前无人机在复杂场景下难以高效构建环境地图和实现远距离全局规划的难题，提出了一种基于概率更新的剪枝可视性地图构建方法和分层规划策略，采用概率更新方式生成栅格地图，通过分层映射提取障碍物边界，结合碰撞检测从而构建出可视性地图。提出一种可视性地图剪枝策略以减小搜索空间，加快路径搜索速度。构建基于搜索和优化的分层规划算法框架，外层规划采用基于探索度的改进A^*算法，通过在代价函数中引入路径探索度，显著提升了无人机在复杂场景下的全局规划表现。内层规划则采用基于最小控制量（MINCO）轨迹表示的轨迹优化方法，以生成满足无人机速度、加速度约束的平滑飞行轨迹。最后，通过实验仿真和实飞验证，结果显示，基于剪枝可视性地图的改进A^*算法相较传统A^*算法，飞行距离减少了12.92%，飞行时间缩短了16.43%，提出的算法能够提升复杂场景下的规划效率和结果最优性。

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.