摘要
传统移动机器人的路径规划算法环境障碍建模复杂且容易陷入局部最小值,而基于采样的快速扩展随机树(RRT)算法通过随机节点快速扩展路径搜索效率低。RRT-Connect算法在RRT算法基础上提升了搜索效率,但存在路径曲折的问题。为此,在RRT-Connect算法基础上通过加入人工势场引导增长方法和目标偏置采样方法,改进算法规划路径的平滑性和速度。为验证改进算法的有效性,与RRT算法、RRT-Connect算法在不同复杂度环境中的执行性能进行比较。仿真实验的结果表明,改进算法在三种不同环境下的路径规划时间和路径规划长度以及标准差稳定性方面均优于其他两种算法。
Traditional path planning algorithms for mobile robots have complex modeling of environmental obstacles and tend to fall into local minima,while the sampling-based rapidly-exploring random tree algorithm is inefficient in searching paths by rapidly exploring random nodes.RRT-Connect algorithm improves the search efficiency on the basis of rapidly-exploring random tree algorithm,but its path is tortuous.In order to solve this problem,the artificial potential field guided growth method and target offset sampling method are added to the RRT-Connect algorithm to improve the smoothness and speed of path planning.To verify the effectiveness of the improved algorithm,it is compared with the performance of RRT algorithm,RRT-Connect algorithm and the improved algorithm in this paper in different complexity environments.The simulation results show that the improved algorithm is superior to the other two algorithms in terms of path planning time,path planning length and standard deviation stability in three different environments.
作者
胡晓阳
赵杰
武炎明
HU Xiaoyang;ZHAO Jie;WU Yanming(Shenyang Ligong University,Shenyang 110159,China;Shenyang Aerospace University,Shenyang 110136,China)
出处
《沈阳理工大学学报》
CAS
2023年第4期26-30,39,共6页
Journal of Shenyang Ligong University
基金
辽宁省博士科研启动基金项目(2020-BS-026)。
