IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2024i11p2067-d1522437.html
   My bibliography  Save this article

Study on Path Planning in Cotton Fields Based on Prior Navigation Information

Author

Listed:
  • Meng Wang

    (College of Engineering, China Agricultural University, Beijing 100083, China
    School of Mechanical and Electrical Engineering, Xinjiang Institute of Engineering, Urumqi 830023, China)

  • Changhe Niu

    (Research Institute of Agricultural Mechanization, Xinjiang Academy of Agricultural Sciences, Urumqi 830091, China)

  • Zifan Wang

    (College of Engineering, China Agricultural University, Beijing 100083, China)

  • Yongxin Jiang

    (Research Institute of Agricultural Mechanization, Xinjiang Academy of Agricultural Sciences, Urumqi 830091, China)

  • Jianming Jian

    (College of Engineering, China Agricultural University, Beijing 100083, China)

  • Xiuying Tang

    (College of Engineering, China Agricultural University, Beijing 100083, China)

Abstract

Aiming at the operation scenario of existing crop coverage and the need for precise row alignment, the sowing prior navigation information of cotton fields in Xinjiang was used as the basis for the study of path planning for subsequent operations to improve the planning quality and operation accuracy. Firstly, the characteristics of typical turnaround methods were analyzed, the turnaround strategy for dividing planning units was proposed, and the horizontal and vertical operation connection methods were put forward. Secondly, the obstacle avoidance strategies were determined according to the traits of obstacles. The circular arc–linear and cubic spline curve obstacle avoidance path generation methods were proposed. Considering the dual attributes of walking and the operation of agricultural machinery, four kinds of operation semantic points were embedded into the path. Finally, path generation software was designed. The simulation and field test results indicated that the operation coverage ratio C R ≥ 98.21% positively correlated with the plot area and the operation distance ratio D R ≥ 86.89% when non-essential reversing and obstacles were ignored. C R and D R were negatively correlated with the number of obstacles when considering obstacles. When considering non-essential reversing, the full coverage of operating rows could be achieved, but D R would be reduced correspondingly.

Suggested Citation

  • Meng Wang & Changhe Niu & Zifan Wang & Yongxin Jiang & Jianming Jian & Xiuying Tang, 2024. "Study on Path Planning in Cotton Fields Based on Prior Navigation Information," Agriculture, MDPI, vol. 14(11), pages 1-20, November.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:11:p:2067-:d:1522437
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/11/2067/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/14/11/2067/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lihong Xu & Jiawei You & Hongliang Yuan, 2023. "Real-Time Parametric Path Planning Algorithm for Agricultural Machinery Kinematics Model Based on Particle Swarm Optimization," Agriculture, MDPI, vol. 13(10), pages 1-17, October.
    2. Tengxiang Yang & Chengqian Jin & Youliang Ni & Zhen Liu & Man Chen, 2023. "Path Planning and Control System Design of an Unmanned Weeding Robot," Agriculture, MDPI, vol. 13(10), pages 1-15, October.
    3. Maria Höffmann & Shruti Patel & Christof Büskens, 2023. "Optimal Coverage Path Planning for Agricultural Vehicles with Curvature Constraints," Agriculture, MDPI, vol. 13(11), pages 1-26, November.
    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. Bingbo Cui & Xinyu Cui & Xinhua Wei & Yongyun Zhu & Zhen Ma & Yan Zhao & Yufei Liu, 2024. "Design and Testing of a Tractor Automatic Navigation System Based on Dynamic Path Search and a Fuzzy Stanley Model," Agriculture, MDPI, vol. 14(12), pages 1-17, November.
    2. Wenbo Wei & Maohua Xiao & Weiwei Duan & Hui Wang & Yejun Zhu & Cheng Zhai & Guosheng Geng, 2024. "Research Progress on Autonomous Operation Technology for Agricultural Equipment in Large Fields," Agriculture, MDPI, vol. 14(9), pages 1-20, August.
    3. Weidong Jia & Kaile Tai & Xiaowen Wang & Xiang Dong & Mingxiong Ou, 2024. "Design and Simulation of Intra-Row Obstacle Avoidance Shovel-Type Weeding Machine in Orchard," Agriculture, MDPI, vol. 14(7), pages 1-22, July.

    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:14:y:2024:i:11:p:2067-:d:1522437. 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.