Continuous Multi-Target Approaching Control of Hyper-Redundant Manipulators Based on Reinforcement Learning

Han Xu, Chen Xue, Quan Chen, Jun Yang () and Bin Liang
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Han Xu: Department of Automation, Tsinghua University, Beijing 100084, China
Chen Xue: China Academy of Aerospace Science and Innovation, Beijing 100176, China
Quan Chen: Department of Automation, Tsinghua University, Beijing 100084, China
Jun Yang: Department of Automation, Tsinghua University, Beijing 100084, China
Bin Liang: Department of Automation, Tsinghua University, Beijing 100084, China

Mathematics, 2024, vol. 12, issue 23, 1-17

Abstract: Hyper-redundant manipulators based on bionic structures offer superior dexterity due to their large number of degrees of freedom (DOFs) and slim bodies. However, controlling these manipulators is challenging because of infinite inverse kinematic solutions. In this paper, we present a novel reinforcement learning-based control method for hyper-redundant manipulators, integrating path and configuration planning. First, we introduced a deep reinforcement learning-based control method for a multi-target approach, eliminating the need for complicated reward engineering. Then, we optimized the network structure and joint space target points sampling to implement precise control. Furthermore, we designed a variable-reset cycle technique for a continuous multi-target approach without resetting the manipulator, enabling it to complete end-effector trajectory tracking tasks. Finally, we verified the proposed control method in a dynamic simulation environment. The results demonstrate the effectiveness of our approach, achieving a success rate of 98.32% with a 134% improvement using the variable-reset cycle technique.

Keywords: hyper-redundant manipulators; multi-target approaching; reinforcement learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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