Manipulator path planning based on improved RRT algorithm

CAI Wentao; DENG Yi; ZHANG Jing; ZHANG Yongbo; RAO Shuang; YANG Kang

doi:10.13873/J.1000-9787(2019)05-0121-04

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Manipulator path planning based on improved RRT algorithm

更新时间：2025-09-11

- Manipulator path planning based on improved RRT algorithm
- Issue 5, Pages: 121-124(2019)
- 作者机构：
  
  1. 西南科技大学信息工程学院
  2. 中国科学技术大学信息科学技术学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2019)05-0121-04
  CLC： TP241
- Published：2019
- 稿件说明：
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CAI Wentao, DENG Yi, ZHANG Jing, et al. Manipulator path planning based on improved RRT algorithm[J]. 2019, (5): 121-124.
DOI：

CAI Wentao, DENG Yi, ZHANG Jing, et al. Manipulator path planning based on improved RRT algorithm[J]. 2019, (5): 121-124. DOI： 10.13873/J.1000-9787(2019)05-0121-04.

摘要

针对快速探索随机树(RRT)路径规划算法缺乏导向性和规划空间增大时算法时间复杂度高的问题

提出一种目标概率偏置与步长控制的改进RRT算法(I-RRT)。I-RRT结合目标概率偏置

以一定概率使采样点偏置为目标点

提高路径规划的导向性

并引入步长控制优化算法

提高运算效率

优化路径。在MATLAB平台建立了算法的仿真实验

结果表明:I-RRT的导向性与算法时间复杂度均优于经典的RRT算法;并在ROS平台上搭建了六自由度机械臂的避障规划与控制实验

实验验证了该算法的有效性。

Abstract

An improved rapidly-exploring random tree（ I-RRT） algorithm with target probability offset and step size control is proposed to solve the problem that RRT path planning algorithm lacks guidance and has high time complexity when the planning space increases. I-RRT combines with target probability offset

takes a certain probability to offset the sampling point as the target point

improves the directivity of path planning

and introduces step-size control optimization algorithm to improve the operation efficiency and optimize the path. The simulation experiment of the algorithm is established on the platform of MATLAB

and the results show that the directivity and time complexity of I-RRT are better than those of classical RRT algorithm. The obstacle avoidance planning and control experiment of 6-degree of freedom（ DOF） manipulator is set up on the platform of ROS

and the validity of the algorithm is verified by the experiment.

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Improved RRT algorithm for manipulator path planning based on dynamic probability sampling

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