Obstacle avoidance path planning for manipulator based on RRT*-DR algorithm

被引：0

作者：

Shang D. ^{[1
,2
,3
]}

Wang J. ^{[1
]}

Fan H. ^{[1
]}

Suo S. ^{[4
]}

机构：

[1] School of Mechanical, Electronic & Information Engineering, China University of Mining and Technology (Beijing), Beijing

[2] Institute of Intelligent Mining and Robotics, China University of Mining and Technology(Beijing), Beijing

[3] Key Laboratory of Intelligent Mining and Robotics, Ministry of Emergency Management, Beijing

[4] Department of Mechanical Engineering, Tsinghua University, Beijing

来源：

Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2024年 / 30卷 / 03期

基金：

中国国家自然科学基金;

关键词：

dynamic sampling region; manipulator; path planning; variable step size;

D O I：

10.13196/j.cims.2022.0902

中图分类号：

学科分类号：

摘要：

To quickly plan a better path for the manipulator in the obstacles environment, a kind of improved RRT∗ -DR path planning algorithm based on RRT∗ was proposed. The entire planning process was divided into two steps: fast exploration of path and optimization of the initial path.A path connecting the starting point and target was found by exploring quickly with a half-goal-guiding expanding mechanism.Then, the dynamic region sampling method was used to always sample in the surrounding range of the current optimal path, and the node tree near the current optimal path was densified, which saved computing resources and made the initial path converge to the asymptotic optimal path quickly through iteration. At the same time, a variable step size mechanism for obstacle-nea-ring nodes was proposed, which selectively reduced the extended step size of the obstacle - nearing nodes, effectively reduced the number of collision detection failures, and improved the algorithm efficiency. The simulation results of MATLAB and the Robot Operating System(ROS) showed that the improved algorithm RRT ∗ -DR could optimize the path in a shorter time, and effectively reducing the path cost. Furthermore, the practicability and effectiveness of the algorithm were verified by the path planning and obstacle avoidance experiment of the real manipulator. © 2024 CIMS. All rights reserved.

引用

页码：1149 / 1160

页数：11

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