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An obstacle avoidance algorithm for space hyper-redundant manipulators using combination of RRT and shape control method

Published online by Cambridge University Press:  03 August 2021

Xiaobo Zhang Open the ORCID record for Xiaobo Zhang [Opens in a new window] ,
Jinguo Liu  and
Yangmin Li Open the ORCID record for Yangmin Li [Opens in a new window]
Xiaobo Zhang
Affiliation:
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang110016, China Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang110169, China University of Chinese Academy of Sciences, Beijing100049, China
Jinguo Liu*
Affiliation:
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang110016, China Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang110169, China
Yangmin Li
Affiliation:
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang110016, China Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang110169, China Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
*
*Corresponding author. E-mail: liujinguo@sia.cn
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Abstract

This paper proposes a kinematic obstacle avoidance algorithm for Space hyper-redundant manipulators, and its basic idea is to use a static and a dynamic curve to constrain the macroshape of the manipulators simultaneously. The static curve is constructed based on a traditional rapidly exploring random tree algorithm, and a backbone curve is utilized as the dynamic curve. For these two curves, two novel shape control methods are proposed to accomplish the shape constraining process. Finally, we verify the reliability and effectiveness of our algorithm through simulations.

Keywords

obstacle avoidanceRRTshape controlspace hyper-redundant manipulators
Type
Research Article
Information
Robotica , Volume 40 , Issue 4 , April 2022 , pp. 1036 - 1069
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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