基于A^(*)算法的无人车路径规划

Path Planning of Unmanned Vehicle Based on A^(*)Algorithm

基于A^(*)算法进行无人车的路径规划,运用栅格图法构建无人车所处宏观运动环境模型,分析了Manhattan距离、Euclidean距离、Chebyshev距离3种常用启发函数,借助MATLAB仿真软件对3种启发函数在无障碍环境、简单障碍环境、凹型陷阱障碍环境和随机复杂障碍环境下的仿真结果进行对比分析。结果表明,一般环境下,采用Manhattan距离的A^(*)算法适用于无人车的路径规划,但在随机复杂障碍环境中可能无法得到全局的最短路径,采用加权Manhattan距离启发函数可以快速实现全局寻优。该结论可为算法的应用提供必要的参考和指导。

The path of unmanned vehicle was mapped out based on A^(*)algorithm,and the macro motion environment model was constructed by using grid method.Three common heuristic functions,namely,Manhattan distance,Euclidean distance and Chebyshev distance,were analyzed and applied to barrier-free environment,simple barrier,depression,and stochastic environment,and their simulation results were compared with the aid of MATLAB.The results indicate that the A^(*)algorithm based on Manhattan distance is applicable in general environment,but not in stochastic environment with complicated barriers because the shortest path may not be obtained.The global optimization can be quickly realized by adopting weighted Manhattan distance.The conclusion provides necessary reference and guidance for the application of the algorithm.