摘要
在移动机器人路径规划研究中,快速探索随机树(RRT)是应用最为广泛的路径规划方法之一.由于RRT采用随机方式产生移动路径上的下一个父节点,其搜索过程具有较大的盲目性;产生的大量冗余无用的父节点也极大地影响了路径规划的整体效率.为了提高渐进最优快速随机搜索树(RRT*)方法的效率,该文提出了一种改进的RRT*算法,其主要思想是应用动态步态延伸帮助RRT*算法找到更优的潜在父节点集,然后结合一种新颖的类三维地图中的驻点信息,在可行的范围内确定最优的父节点,从而更为高效地规划出一条从初始点到目标点的可行路径.最后在不同环境下的实验验证改进后的RRT*算法的有效性.
In the research of mobile robot path planning,rapidly-exploring random tree(RRT)is one of the most widely used path planning methods.Because RRT uses random method to generate the next parent node on the moving path,its search process has great blindness.A large number of redundant and useless parent nodes also greatly affect the overall efficiency of path planning.In order to improve the efficiency of the RRT*method,an improved RRT*algorithm is proposed in this paper.The main idea is to use dynamic gait extension to help the RRT*algorithm find a better set of potential parent nodes,and then determine the optimal parent node within the feasible range by combining the stagnation point information in a novel three-dimensional map.Thus more efficient planning a feasible path from the initial point to the target point.Finally,experiments in different environments verify the effectiveness of the improved RRT*algorithm.
作者
印峰
谢青松
YIN Feng;XIE Qingsong(School of Automation and Electronic Information,Xiangtan University,Xiangtan 411105,China)
出处
《湘潭大学学报（自然科学版）》
CAS
2022年第4期22-31,共10页
Journal of Xiangtan University(Natural Science Edition)
基金
国家自然科学基金(U19A2083)。
