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FBi-RRT: a path planning algorithm for manipulators with heuristic node expansion

Published online by Cambridge University Press:  27 December 2023

Guangzhou Xiao Open the ORCID record for Guangzhou Xiao [Opens in a new window] ,
Lixian Zhang ,
Tong Wu ,
Yuejiang Han ,
Yihang Ding  and
Chengzhe Han
Guangzhou Xiao
Affiliation:
School of Astronautics, Harbin Institute of Technology, Harbin, China
Lixian Zhang*
Affiliation:
School of Astronautics, Harbin Institute of Technology, Harbin, China
Tong Wu
Affiliation:
School of Astronautics, Harbin Institute of Technology, Harbin, China
Yuejiang Han
Affiliation:
School of Astronautics, Harbin Institute of Technology, Harbin, China
Yihang Ding
Affiliation:
School of Astronautics, Harbin Institute of Technology, Harbin, China
Chengzhe Han
Affiliation:
School of Astronautics, Harbin Institute of Technology, Harbin, China
*
Corresponding author: Lixian Zhang; Email: lixianzhang@hit.edu.cn
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Abstract

This paper is concerned with the problem of collision-free path planning for manipulators in multi-obstacle scenarios. Aiming at overcoming the deficiencies of existing algorithms in excessive time consumption and poor expansion quality, a path planning algorithm named Fast Bi-directional Rapidly-exploring Random Tree (FBi-RRT) with novel heuristic node expansion is proposed, which includes a selective-expansion strategy and a vertical-exploration strategy. The selective-expansion strategy is designed to guide the selection of the nearest-neighbor node to avoid the repeated expansion failure, thereby shortening the overall planning time. Also, the vertical-exploration strategy is developed to regulate the expansion direction of the collision nodes to escape from the obstacle space with less blindness, thus improving the expansion quality and further reducing time cost. Compared with previous planning algorithms, FBi-RRT can generate a feasible path for manipulators in a drastically shorter time. To validate the effectiveness of the proposed heuristic node expansion, FBi-RRT is conducted on a 6-DOF manipulator and tested in five scenarios. The experimental results demonstrate that FBi-RRT outperforms the existing methods in time consumption and expansion quality.

Keywords

path planningrapidly exploring random treerobotic manipulatorsampling-based algorithmtime consumption
Type
Research Article
Information
Robotica , Volume 42 , Issue 3 , March 2024 , pp. 644 - 659
Copyright
© The Author(s), 2023. Published by Cambridge University Press

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