摘要
提出了一种带区域限制并由人工势场引导的改进快速探索随机树RRT路径规划算法,克服了传统RRT算法在路径搜索过程中的算法效率低、采样随机性大等问题。首先在采样开始前根据起始位置与目标位置,规划包含起始点与目标点的有界连通区域作为采样区域;然后根据节点与障碍物的相对关系自适应调整选择目标点作为随机点的概率;最后在人工势场法引导下,优化采样过程,并根据循环次数调整采样区域。仿真实验表明,相对于初始的RRT算法,该算法所耗时间更短,平均节点数更少,路径搜索效率更高。
The article proposes an improved RRT path planning algorithm with area limitation and guided by artificial potential field.The developed algorithm solves the problems of low efficiency and large sampling randomness of the traditional RRT algorithm in the path search process.Firstly,before the start of sampling,a bounded connected area including the starting point and the target point is planned as the sampling area.Then the probability of randomly selecting the target point is adaptively adjusted according to the relative relationship between the node and the obstacle.Finally,the sampling area is adjusted according to the number of cycles,and the sampling process is optimized under the guidance of the artificial potential field method.Simulation results show that,comparing with the initial RRT algorithm,the algorithm proposed in this paper takes less time,requires fewer average nodes,and has higher path search efficiency.
作者
徐劲力
曹其
XU Jinli;CAO Qi(School of Mechanical and Electronic Engineering,Wuhan University of Technology,Wuhan 430000,China)
基金
中央高校基本科研业务费资助项目(205204004)
关键词
区域限制
人工势场
RRT
路径规划
area limitation
artificial potential field
RRT
path planning
作者简介
徐劲力(1965-),男,湖北武汉人,武汉理工大学机电工程学院教授,博士.
