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Classification-design-optimization integrated picking robots: a review

Author

Listed:
  • Jingyang Xiang

    (Southwest University
    Zhejiang University)

  • Lianguo Wang

    (Southwest University)

  • Li Li

    (Southwest University)

  • Kee-Hung Lai

    (The Hong Kong Polytechnic University)

  • Wei Cai

    (Southwest University
    The Hong Kong Polytechnic University
    Yibin Academy of Southwest University)

Abstract

Robot technology is considered one of the most promising technologies to achieve intelligent production, with picking robots being the most common type. Picking robots are highly integrated mechatronic systems, which autonomously complete tasks including picking, carrying, and sorting. The application of picking robots enhances the efficiency of production across various environments. In this paper, a classification-design-optimization-application integrated framework of picking robots is addressed, contributing to theoretical research and application of picking robots. Classification of picking robot is established and analyzed considering the differences of overall form and end-effector to guide the development of research strategies and approaches of picking robot. Design of picking robot is described from different aspects of the target, structure, monitoring, and control design. Additionally, the commonly used optimization methods for picking robots, including structural parameters, kinematics and dynamics, and energy consumption, are discussed. Finally, the application of picking robots under different environments is expounded, and the challenges and prospects of picking robots are highlighted. This study could present theoretical support and application measures for picking robot study and technology development.

Suggested Citation

  • Jingyang Xiang & Lianguo Wang & Li Li & Kee-Hung Lai & Wei Cai, 2024. "Classification-design-optimization integrated picking robots: a review," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 2979-3002, October.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:7:d:10.1007_s10845-023-02201-5
    DOI: 10.1007/s10845-023-02201-5
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    References listed on IDEAS

    as
    1. Anand Srivastav & Hartmut Schroeter & Christoph Michel, 2001. "Approximation Algorithms for Pick-and-Place Robots," Annals of Operations Research, Springer, vol. 107(1), pages 321-338, October.
    2. 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.
    3. Andy Ham, 2020. "Transfer-robot task scheduling in flexible job shop," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1783-1793, October.
    Full references (including those not matched with items on IDEAS)

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