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Grasping Force Optimization and DDPG Impedance Control for Apple Picking Robot End-Effector

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  • Xiaowei Yu

    (School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Wei Ji

    (School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
    The Key Laboratory for Agricultural Machinery Intelligent Control and Manufacturing of Fujian Education Institutions, Wuyishan 354300, China)

  • Hongwei Zhang

    (School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Chengzhi Ruan

    (The Key Laboratory for Agricultural Machinery Intelligent Control and Manufacturing of Fujian Education Institutions, Wuyishan 354300, China
    College of Mechanical and Electrical Engineering, Wuyi University, Wuyishan 354300, China)

  • Bo Xu

    (School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Kaiyang Wu

    (School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China)

Abstract

To minimize mechanical damage caused by an apple picking robot end-effector during the apple grasping process, and on the basis of optimizing the minimum stable grasping force of apple, a variable impedance control strategy based on a reinforcement learning deep deterministic policy gradient (DDPG) algorithm is proposed to achieve compliant grasping control for apples. Firstly, according to the apple contact force model, the gradient flow algorithm is adopted to optimize grasping force in terms of the friction cone, force balancing condition, and stability assessment index and to obtain a minimum stable grasping force for apples. Secondly, based on the analysis of the influence of impedance parameters on the control system, a variable impedance control based on the DDPG algorithm is designed, with the reward function adopted so as to improve the control performance. Then, the improved control strategy is used to train the optimized impedance control. Finally, simulation and experimental results indicate that the proposed variable impedance control outperforms the traditional impedance control by reducing the peak grasping force from 4.49 N to 4.18 N while achieving a 0.6 s faster adjustment time and a 0.24 N narrower grasping force fluctuation range. The improved impedance control successfully tracks desired grasping forces for apples of varying sizes and significantly reduces mechanical damage during apple harvesting.

Suggested Citation

  • Xiaowei Yu & Wei Ji & Hongwei Zhang & Chengzhi Ruan & Bo Xu & Kaiyang Wu, 2025. "Grasping Force Optimization and DDPG Impedance Control for Apple Picking Robot End-Effector," Agriculture, MDPI, vol. 15(10), pages 1-22, May.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:10:p:1018-:d:1651666
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    References listed on IDEAS

    as
    1. Hongwei Zhang & Wei Ji & Bo Xu & Xiaowei Yu, 2024. "Optimizing Contact Force on an Apple Picking Robot End-Effector," Agriculture, MDPI, vol. 14(7), pages 1-16, June.
    2. Bo Xu & Xiang Cui & Wei Ji & Hao Yuan & Juncheng Wang, 2023. "Apple Grading Method Design and Implementation for Automatic Grader Based on Improved YOLOv5," Agriculture, MDPI, vol. 13(1), pages 1-18, January.
    3. Peng Jia & Lei Wu & Gang Wang & Wei Na Geng & Feihong Yun & Ning Zhang, 2019. "Grasping Torque Optimization for a Dexterous Robotic Hand Using the Linearization of Constraints," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-17, November.
    4. Kaiwen Chen & Tao Li & Tongjie Yan & Feng Xie & Qingchun Feng & Qingzhen Zhu & Chunjiang Zhao, 2022. "A Soft Gripper Design for Apple Harvesting with Force Feedback and Fruit Slip Detection," Agriculture, MDPI, vol. 12(11), pages 1-26, October.
    Full references (including those not matched with items on IDEAS)

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