融合改进A^(*)与DWA的轮式机器人路径规划

Path planning of wheeled robot based on improved A^(*) and DWA

针对轮式机器人在路径规划中存在的问题,提出融合改进A^(*)和DWA的轮式机器人路径规划方法。首先针对A^(*)算法冗余点多、耗时长、拐点多等问题,提出双向A^(*)和跳点算法相结合来提升全局路径规划效率;其次针对DWA寻找的路径非最优以及无法应变环境的问题,选取全局最优路径上优化后的跳点作为DWA的关键点,引入关键点评价子函数和自适应速度权值。融合改进的A^(*)和DWA算法,既保证了全局路径最优,还可根据障碍物数量和分布,调整速度权值,兼顾速度和安全性。经在MATLAB仿真环境中验证:融合改进A^(*)和DWA的轮式机器人路径规划方法与传统A^(*)算法相比,大幅减少了规划时长和拓展节点数量,效率明显提升;与DWA算法相比,加入全局路径关键点和自适应速度权值,能遵循全局最优路径以及降低了运行时间,提升效率。

Aiming at the problems of path planning for wheeled robots,a path planning method for wheeled robots based on improved A^(*)and DWA is proposed.Firstly,aiming at the problems of redundant points,time-consuming and inflection points of A^(*)algorithm,a combination of bidirectional A^(*)and hop point algorithm is proposed to improve the efficiency of global path planning;Secondly,aiming at the problem that the path searched by DWA is not optimal and cannot adapt to the environment,the optimized jump point on the global optimal path is selected as the key point of DWA,and the key point evaluation sub function and adaptive speed weight are introduced.The fusion of improved A^(*)and DWA algorithm not only ensures the global optimal path,but also adjusts the speed weight according to the number and distribution of obstacles,taking into account the speed and safety.In the MATLAB simulation environment,it shows that:compared with the traditional A^(*)algorithm,the planning time and the number of expansion nodes are greatly reduced,and the efficiency is significantly improved;Compared with DWA algorithm,adding global path key points and adaptive speed weights can follow the global optimal path,reduce the running time and improve the efficiency.