融合优化A^(*)算法与动态窗口法的动态路径规划算法研究

Research on dynamic path planning algorithm based on optimized A^(*) algorithm and dynamic window approach

针对机器人路径规划对于全局最优性以及路径平滑度的性能要求,提出了一种新的基于跳点搜索的优化A^(*)算法和动态窗口法的融合算法。在跳点搜索法基础上,该融合算法设计了由曼哈顿和欧氏距离结合的新的距离评估函数对A^(*)算法进行优化,以此获取全局路径信息;然后以动态窗口法为核心,快速地规划出一条具有高平滑度的全局最优路径。仿真实验表明:新的融合算法有效解决了优化A^(*)算法规划的路径转折处曲率非连续的问题,提高了路径的平滑程度和全局最优性。最后在搭建的实际环境中进一步验证了算法的有效性,对机器人导航与路径规划有一定的应用价值。

In order to meet the performance requirements of global optimality and path smoothness in robot path planning,a new fusion algorithm of optimized A^(*) algorithm based on jump point search and dynamic window approach is proposed.Based on jump point search,the fusion algorithm designs a new distance evaluation function combining with Manhattan and Euclidean distance to optimize the A^(*) algorithm,which obtains global path information;and then the dynamic window approach as the core is taken to quickly plan a global optimal path with high smoothness.Simulation results show that the new fusion algorithm proposed can not only effectively solve the problem of non-continuous curvature and excessive turning angle at the turning points of the path planned by jump-A^(*) algorithm,but also improve the smoothness of the path and the global optimality.Finally,the effectiveness of the algorithm is further verified in the actual environment,which has a certain application value for robot navigation and path planning.