基于六向搜索A^(*)算法的移动机器人路径规划

Path Planning of Mobile Robot Based on Six-Way Search A^(*) Algorithm

针对移动机器人利用传统A^(*)算法在复杂环境中进行路径规划时,存在着扩展节点数多导致的搜索效率低,以及路径平滑性不足等问题,提出了一种基于六向搜索的A^(*)算法。首先,在传统A^(*)算法启发函数的基础上利用曼哈顿距离进行加权,减少了算法的搜索时间和扩展节点数;其次,对传统A^(*)算法搜索策略进行改进,提出一种六向搜索策略,进一步减少算法扩展节点数,并同时提升路径平滑性;最后,利用路径平滑策略来对规划出来的路径进行平滑处理。实验结果表明,基于六向搜索的A^(*)算法在不同地图规模的仿真环境中都能获得较高的搜索效率,且扩展节点数更少、转折角度更小、更有利于移动机器人的路径规划。

Aiming at the problems of low search efficiency and lack of path smoothness caused by the large number of extended nodes when the traditional A^(*)algorithm is used for mobile robot path planning in complex environment,an A^(*)algorithm based on six-way search is proposed.Firstly,on the basis of the heuristic function of the traditional A^(*)algorithm,the Manhattan distance is weighted to reduce the search time and the number of extended nodes.Secondly,the traditional A^(*)algorithm search strategy is improved,and a six-way search strategy is proposed to further reduce the number of algorithm expansion nodes and improve the path smoothness at the same time.Finally,the path smoothing strategy is used to smooth the planned path.The experimental results show that the A^(*)algorithm based on six-way search can obtain higher search efficiency in simulation environments of different map scales,and the number of extended nodes is less,the turning angle is smaller,and it is more conducive to the path planning of mobile robots.