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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Han Xu & Chen Xue & Quan Chen & Jun Yang & Bin Liang, 2024. "Continuous Multi-Target Approaching Control of Hyper-Redundant Manipulators Based on Reinforcement Learning," Mathematics, MDPI, vol. 12(23), pages 1-17, December.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:23:p:3822-:d:1535472
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    References listed on IDEAS

    as
    1. Xiaotong Hua & Guolei Wang & Jing Xu & Ken Chen, 2021. "Reinforcement learning-based collision-free path planner for redundant robot in narrow duct," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 471-482, February.
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