In the unstructured scene with multiple obstacles， the traditional hybrid A* algorithm has the problems of low computational efficiency and poor path smoothness. For these problems， this paper proposes a hybrid A* path planning method based on the density-based clustering （DBSCAN） and the dichotomy. Firstly， based on the DBSCAN algorithm， an obstacle clustering method is designed to simplify the multi-obstacle unstructured scene， so as to avoid invalid node expansion of the hybrid A* algorithm near the U-shaped obstacle group， and to effectively improve the efficiency of the algorithm. Then， a dichotomy-based state node expansion strategy is proposed， which can search a smoother path without significantly increasing the computational complexity of the hybrid A* algorithm. Finally， simulation is performed on MATLAB. The results show that in the multi-obstacle unstructured scene， the improved hybrid A* algorithm proposed in this paper can significantly improve the computational efficiency and the path smoothness.
