IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v15y2025i10p1018-d1651666.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/15/10/1018/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/15/10/1018/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    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)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jin Yuan & Wei Ji & Qingchun Feng, 2023. "Robots and Autonomous Machines for Sustainable Agriculture Production," Agriculture, MDPI, vol. 13(7), pages 1-4, July.
    2. Renzhong Wang & Sunyang Zhang & Yifei Yang & Yifang Wen & Xiaodong Sun & Zhongzhuang Zhou & Yuting Li, 2025. "Overview of Deadbeat Predictive Control Technology for Permanent Magnet Synchronous Motor System," Energies, MDPI, vol. 18(17), pages 1-25, September.
    3. Yayun Shen & Yue Shen & Yafei Zhang & Chenwei Huo & Zhuofan Shen & Wei Su & Hui Liu, 2025. "Research Progress on Path Planning and Tracking Control Methods for Orchard Mobile Robots in Complex Scenarios," Agriculture, MDPI, vol. 15(18), pages 1-40, September.
    4. Yang Li & Kuo Zhang & Jianping Li & Xin Yang & Pengfei Wang & Hongjie Liu, 2025. "Parameter Optimization and Experimental Study of an Apple Postharvest Damage-Reducing Conveyor Device Based on Airflow Cushioning Technology," Agriculture, MDPI, vol. 15(8), pages 1-23, April.
    5. Rafael Goulart & Dennis Jarvis & Kerry B. Walsh, 2023. "Evaluation of End Effectors for Robotic Harvesting of Mango Fruit," Sustainability, MDPI, vol. 15(8), pages 1-18, April.
    6. Laraib Haider Naqvi & Badrinath Balasubramaniam & Jiaqiong Li & Lingling Liu & Beiwen Li, 2025. "Four-Dimensional Hyperspectral Imaging for Fruit and Vegetable Grading," Agriculture, MDPI, vol. 15(15), pages 1-17, August.
    7. Zixuan Xiang & Yu Miao & Yuting Zhou & Feng Li, 2025. "Design and Optimization of a High-Efficiency Lightweight Permanent Magnet In-Wheel Motor with Torque Performance Improvement," Energies, MDPI, vol. 18(17), pages 1-20, August.
    8. Zohaib Khan & Yue Shen & Hui Liu, 2025. "ObjectDetection in Agriculture: A Comprehensive Review of Methods, Applications, Challenges, and Future Directions," Agriculture, MDPI, vol. 15(13), pages 1-36, June.
    9. Hang Zhou & Jin Gao & Fan Zhang & Junxiong Zhang & Song Wang & Chunlong Zhang & Wei Li, 2023. "Evaluation of Cutting Stability of a Natural-Rubber-Tapping Robot," Agriculture, MDPI, vol. 13(3), pages 1-23, February.
    10. Yiyong Jiang & Ruochen Wang & Renkai Ding & Zeyu Sun & Yu Jiang & Wei Liu, 2025. "Research Review of Agricultural Machinery Power Chassis in Hilly and Mountainous Areas," Agriculture, MDPI, vol. 15(11), pages 1-37, May.
    11. Jinye Gao & Jun Sun & Xiaohong Wu & Chunxia Dai, 2025. "RDM-YOLO: A Lightweight Multi-Scale Model for Real-Time Behavior Recognition of Fourth Instar Silkworms in Sericulture," Agriculture, MDPI, vol. 15(13), pages 1-19, July.
    12. Chongyang Han & Jinhong Lv & Chengju Dong & Jiehao Li & Yuanqiang Luo & Weibin Wu & Mohamed Anwer Abdeen, 2024. "Classification, Advanced Technologies, and Typical Applications of End-Effector for Fruit and Vegetable Picking Robots," Agriculture, MDPI, vol. 14(8), pages 1-37, August.
    13. Aichen Wang & Yuanzhi Xu & Dong Hu & Liyuan Zhang & Ao Li & Qingzhen Zhu & Jizhan Liu, 2025. "Tomato Yield Estimation Using an Improved Lightweight YOLO11n Network and an Optimized Region Tracking-Counting Method," Agriculture, MDPI, vol. 15(13), pages 1-20, June.
    14. Haoran Zhu & Huanhuan Qin & Zicheng Qiu & Xinwen Chen & Jinlin Xue & Xingjian Gu & Mingzhou Lu, 2025. "A Compliant Active Roller Gripper with High Positional Offset Tolerance for Delicate Spherical Fruit Handling," Agriculture, MDPI, vol. 15(2), pages 1-18, January.
    15. Haonan Shi & Gaoming Xu & Wei Lu & Qishuo Ding & Xinxin Chen, 2024. "An Electric Gripper for Picking Brown Mushrooms with Flexible Force and In Situ Measurement," Agriculture, MDPI, vol. 14(7), pages 1-15, July.
    16. Long Su & Ruijia Liu & Kenan Liu & Kai Li & Li Liu & Yinggang Shi, 2023. "Greenhouse Tomato Picking Robot Chassis," Agriculture, MDPI, vol. 13(3), pages 1-23, February.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:15:y:2025:i:10:p:1018-:d:1651666. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.