优化改进A^(*)和动态窗口法的机器人路径规划

Robot Path Planning Based on Optimize and Improve A^(*) and Dynamic Window Approach

针对传统A^(*)算法规划的路径理论上不是最优,路径中存在较多拐点和冗余路段等问题,通过3个步骤进行改进。首先,扩展A^(*)算法的搜索邻域,用于减少路径规划子节点数,打破了路径搜索方向仅为0.25π的限制;其次,改进代价函数,提高搜索效率,对改进A^(*)算法规划的路径通过提取关键点进行优化,剔除冗余节点和冗余路段;最后,将优化后的路径以相邻节点分段使用改进动态窗口法进行规划,融合算法分别与传统A^(*)算法与传统动态窗口法对比。研究结果表明,融合算法安全性更高,路径更短,且能够实现避开动态障碍物。

The path planned by traditional A^(*)algorithm is not optimal in theory, because there are many inflection points and redundant sections in the path, follow these three steps to make improvements.First, the search neighborhood of A^(*)algorithm is extended to reduce the number of child nodes in the path planning, breaking the restriction that the path search direction is only 0.25π.Then, the path planned by the improved A^(*)algorithm is optimized by extracting key points and eliminating redundant nodes and redundant sections;Last, the optimized path is planned by using the dynamic window method in segments of adjacent nodes, and the fusion algorithm is compared with the traditional A^(*)algorithm and the traditional dynamic window method respectively.Show that the fusion algorithm has higher security, shorter path, and can avoid dynamic obstacles.