基于信息启发的目标导向Bi-RRT机器人路径规划

Robot path planning of goal-directed Bi-RRT based on information inspiration

针对双向快速搜索随机树(Bi-RRT)算法节点扩展的随机性和盲目性导致路径规划效率低、路径粗糙的问题,提出一种基于信息启发的目标导向Bi-RRT算法。首先,为降低节点扩展的随机性和盲目性,优化了树节点的扩展方式,采用回归分析生成的节点信息优化扩展节点评价函数,以强化节点生长的目标趋向性,并由节点与环境代价约束扩展方向。然后,采用分支定界思想剔除初始路径中的冗余节点,得到满足最大转向角约束的路径,并运用B样条曲线进行路径平滑,提高路径的平滑性和连续性。最后,基于MATLAB仿真平台对本文算法和经典路径规划算法在不同环境中进行了实验对比,实验结果验证了本文算法的有效性及可执行性。

Aiming at the problems of low efficiency and rough path planning due to the randomness and blindness of node expansion in Bi-directional rapidly-exploring random tree algorithm,this paper proposes a goal-directed Bi-RRT algorithm based on information inspiration.In order to reduce the randomness and blindness of node expansion,the tree node expansion method is optimized.The node information generated by regression analysis is used to optimize the extended node evaluation function to strengthen the target tropism of node growth,and the expansion direction is constrained by node and environmental cost.The redundant nodes in the initial path are eliminated by branch and bound method,and the path satisfying the maximum steering Angle constraint is obtained.The cubic B-spline curve is used to smooth the path to improve the smoothness and continuity of the path.Finally,the proposed algorithm is compared with other classical algorithms in different environments based on the MATLAB simulation platform,and the experimental results verify the effectiveness and enforceability of the proposed algorithm.