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Study on Trajectory Optimization for a Flexible Parallel Robot in Tomato Packaging

Author

Listed:
  • Tianci Guo

    (College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
    Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Jiangbo Li

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    Key Laboratory of Cold Chain Logistics Technology for Agro-Product, Ministry of Agriculture and Rural Affairs, Beijing 100097, China)

  • Yizhi Zhang

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Letian Cai

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Qicheng Li

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

Abstract

Currently, flexible robots, exemplified by parallel robots, play a crucial role in the automated packaging of agricultural products due to their rapid, accurate, and stable characteristics. This research systematically explores trajectory planning strategies for parallel robots in the high-speed tomato-grabbing process. Kinematic analysis of the parallel robot was conducted using geometric methods, deriving the coordinates of each joint at various postures, resulting in a kinematic forward solution model and corresponding equations, which were verified with data. To address the drawbacks of the point-to-point “portal” trajectory in tomato grabbing, a 3-5-5-3 polynomial interpolation method in joint space was proposed to optimize the path, enhancing trajectory smoothness. To improve the efficiency of the tomato packaging process, a hybrid algorithm combining particle swarm optimization (PSO) and genetic algorithms (GA) was developed to optimize the operation time of the parallel robot. Compared to traditional PSO, the proposed algorithm exhibits better global convergence and is less likely to fall into local optima, thereby ensuring a smoother and more efficient path in the robot-grabbing tomato process and providing technical support for automated tomato packaging.

Suggested Citation

  • Tianci Guo & Jiangbo Li & Yizhi Zhang & Letian Cai & Qicheng Li, 2024. "Study on Trajectory Optimization for a Flexible Parallel Robot in Tomato Packaging," Agriculture, MDPI, vol. 14(12), pages 1-17, December.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:12:p:2274-:d:1541644
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    References listed on IDEAS

    as
    1. Xiaomei Hu & Zhaoren Pan & Shunke Lv, 2019. "Picking Path Optimization of Agaricus bisporus Picking Robot," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-16, September.
    2. Xiong Zhao & Di Cheng & Wenxun Dong & Xingxiao Ma & Yongsen Xiong & Junhua Tong, 2022. "Research on the End Effector and Optimal Motion Control Strategy for a Plug Seedling Transplanting Parallel Robot," Agriculture, MDPI, vol. 12(10), pages 1-21, October.
    3. Myong Song Choe & Kwan Sik Jang & Yong Ho Kim & Kyong Hyok Kim & Won-Chol Yang & Jaime Gallardo Alvarado, 2023. "An Approach for Elliptical Trajectory Planning with Vertical Straight Line Segments of Pick-and-Place Robot Operation with Height Clearance," Mathematical Problems in Engineering, Hindawi, vol. 2023, pages 1-12, June.
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