基于BRS-RRT^(*)算法的移动机器人路径规划

Path Planning of AGV Based on BRS-RRT^(*)Algorithm

针对Informed-RRT^(*)算法在路径规划中收敛速度低、目标性差且所得轨迹不平滑的局限性,提出一种面向目标的区域采样双向RRT^(*)算法。首先,引入双向贪婪搜索策略获得采样点,加快算法搜索速度的同时改变随机树的扩展规则,增强其目标导向性;其次,得到初始解后,于轨迹节点附近展开形成启发式采样区域,在该区域内通过节点重构策略不断迭代,优化路径长度;最后,采用中间点插值和三次样条曲线相结合的方法,完成对路径的平滑处理。仿真实验表明,改进算法能够在不同环境地图中以更短的运行时间生成节点数更少、代价更小、更为平滑的路径。

In response to the limitations of the Informed-RRT^(*)algorithm in path planning,such as slow convergence speed,inadequate targeting and non-smooth trajectory,a bidirectional regional sampling RRT^(*)algorithm is proposed.Firstly,the bidirectional greedy search approach is introduced to expedite the identification of sampling points while simultaneously adapting the expansion rules governing the growth of the random tree.This dual-pronged strategy not only accelerates the search process but also enhances its alignment with specified objectives.Secondly,following the initial solution establishment,a heuristic sampling region is introduced to proximate to trajectory nodes,and the path length is continuously iteratively optimized through node reconstruction strategy within this region.Finally,a combination of intermediate point interpolation and cubic spline curve techniques is employed to smooth the path.Simulation results demonstrate that the proposed algorithm can generate fewer nodes,lower costs and smoother paths in different environment maps with less runtime.