Path planning algorithms are key to enabling mobile robot navigation. In view of the deficiencies of traditional path planning algorithms in orchard environments in terms of node traversal, search efficiency, path smoothness and obstacle avoidance ability, this paper proposes an improved path planning method combining the A* algorithm and the DWA algorithm, which effectively improves the global optimality and real-time obstacle avoidance of the planned path. Firstly, a two-dimensional raster map is constructed using three-dimensional point cloud data to provide an accurate environmental model for the navigation robot. The neighborhood search method of the traditional A* algorithm is optimized through the rectangular expansion search strategy. Combined with the selection method of critical path nodes and the path smoothing technology based on dynamic tangent circles, a global path that meets the operation requirements of orchards is generated. Optimize the evaluation function of the traditional DWA algorithm, introduce factors such as heading angle, path deviation and obstacle information, and improve the global orientation and local response ability of obstacle avoidance decisionmaking. Finally, A fusion architecture of the improved A* algorithm and the improved DWA algorithm is constructed to enable coordinated global navigation and local avoidance. The simulation results show that the improved algorithm in this paper has significant advantages in terms of path planning efficiency, path quality and obstacle avoidance, meeting the actual needs of mobile robot path planning in the orchard environment and supporting intelligent orchard management.