基于路网的车辆路径规划融合算法究

针对室外区域非结构化道路环境，为了同时解决全局路径规划问题和局部路径规划问题，提出一种基于Floyd算法、A*算法和人工势场法的路径规划融合算法。采用A*算法规划可行路径，基于Floyd全局最优距离和权重数据递归求出最优路径；改进人工势场法，设计分段力场结合自适应角度函数，规划避障路径；基于三次多项式曲线拟合得到满足动力学约束的行车轨迹。采用MATLAB/Simulink进行路径规划仿真，结果表明：路径规划融合算法根据前方检测环境能准确规划出全局绕路轨迹和局部避障轨迹，单次规划时间分别为1.14 ms和45 ms，满足实时性要求。实车实验结果表明仿真结果准确，验证了实路径规划融合算法的有效性和算法的鲁棒性

Vehicle Path Planning Fusion Algorithm Based on Road Network

Aimed at the unstructured outdoor road environment, in order to solve the global and local path planning problems at the same time, a path planning fusion algorithm based on Floyd algorithm, A* algorithm and artificial potential field method was proposed. A*algorithm was used to plan feasible path. The optimal path was recursively obtained based on Floyd global optimal distance and weight data. Based on the improved artificial potential field method, the segmented force field combined with the adaptive angle function was designed to plan the obstacle avoidance path. Based on the cubic polynomial curve, the driving trajectory satisfying the dynamic constraints was obtained by fitting. MATLAB/Simulink was used for path planning simulation. The results show that the path planning fusion algorithm can be used to accurately plan the global detour path or the local obstacle avoidance path according to the front detection environment, and the single planning time is 1.14 ms and 45 ms respectively, which meets the real time requirements. The real vehicle experiment results show that the simulation results are accurate, which proves the effectiveness and robustness of the real path planning fusion algorithm