基于混合A*和可变半径RS曲线的自动泊车路径优化方法

Path planning is an important part of automatic parking systems. It is the key to ensuring parking safety, shortening driving distance, and improving ride comfort. However, current automatic parking planning systems face certain technical challenges, such as narrow driving space, the presence of several obstacles, and difficulty in path search. Furthermore, the fixed radius of the search curve easily leads to discontinuous curvature at the path joints, increasing the difficulty of path-following control and the degree of tire wear. Accordingly, these factors complicate parking-path planning. Thus, in this study, a path optimization algorithm for automatic parking was developed based on hybrid A* and the Reeds-Shcpp curve with variable radius. Adjusting the curve radius can improve path-search ability and flexibility in complex scenarios. Moreover, a path optimization method based on the segmented Bezier curve and gradient descent was developed. Continuous multi-order derivatives were used to optimize the curvature of the searched path, and gradient descent was used to ensure path safety and avoid obstacles. Thus, discontinuous curvature changes at the point where a straight line meets an arc can be handled. The proposed parking planning method, which combines path search and path optimization, can effectively meet parking needs in complex scenarios. Finally, based on MATLAB and PanoSim virtual system, which was independently developed by Vehicle Controls and Intelligence Lab (VCI Lab), a joint simulation environment was developed to test and verify the proposed method under a variety of automatic parking conditions. The results demonstrate that the variable curve radius for global path search yields a shorter path that is easier to follow, and achieves considerable flexibility. The path optimization method based on the segmented Bezier curve and gradient descent can effectively eliminate sudden curvature changes, constrain path curvature, and ensure safe driving.