IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v15y2025i3p266-d1577490.html
   My bibliography  Save this article

Agricultural Machinery Path Tracking with Varying Curvatures Based on an Improved Pure-Pursuit Method

Author

Listed:
  • Jiawei Zhou

    (College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China
    National Engineering Technology Research Center of State Forestry and Grassland Administration on Forestry and Grassland Machinery for Hilly and Mountainous Areas, Hangzhou 311300, China
    Key Laboratory of Agricultural Equipment for Hilly and Mountainous Areas in South-Eastern China (Co-Construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Hangzhou 311300, China)

  • Junhao Wen

    (College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China
    National Engineering Technology Research Center of State Forestry and Grassland Administration on Forestry and Grassland Machinery for Hilly and Mountainous Areas, Hangzhou 311300, China
    Key Laboratory of Agricultural Equipment for Hilly and Mountainous Areas in South-Eastern China (Co-Construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Hangzhou 311300, China)

  • Liwen Yao

    (College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China
    National Engineering Technology Research Center of State Forestry and Grassland Administration on Forestry and Grassland Machinery for Hilly and Mountainous Areas, Hangzhou 311300, China
    Key Laboratory of Agricultural Equipment for Hilly and Mountainous Areas in South-Eastern China (Co-Construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Hangzhou 311300, China)

  • Zidong Yang

    (College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China
    National Engineering Technology Research Center of State Forestry and Grassland Administration on Forestry and Grassland Machinery for Hilly and Mountainous Areas, Hangzhou 311300, China
    Key Laboratory of Agricultural Equipment for Hilly and Mountainous Areas in South-Eastern China (Co-Construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Hangzhou 311300, China)

  • Lijun Xu

    (College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China
    National Engineering Technology Research Center of State Forestry and Grassland Administration on Forestry and Grassland Machinery for Hilly and Mountainous Areas, Hangzhou 311300, China
    Key Laboratory of Agricultural Equipment for Hilly and Mountainous Areas in South-Eastern China (Co-Construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Hangzhou 311300, China)

  • Lijian Yao

    (College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China
    National Engineering Technology Research Center of State Forestry and Grassland Administration on Forestry and Grassland Machinery for Hilly and Mountainous Areas, Hangzhou 311300, China
    Key Laboratory of Agricultural Equipment for Hilly and Mountainous Areas in South-Eastern China (Co-Construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Hangzhou 311300, China)

Abstract

The current research on path tracking primarily focuses on improving control algorithms, such as adaptive and predictive models, to enhance tracking accuracy and stability. To address the issue of low tracking accuracy caused by variable-curvature paths in automatic navigation within agricultural environments, this study proposes a fuzzy control-based path-tracking method. Firstly, a pure-pursuit model and a kinematic model were established based on a Four-Wheel Independent Steering and Four-Wheel Independent Driving (4WIS-4WID) structure. Secondly, a fuzzy controller with three inputs and one output was designed, using the lateral deviation, d e ; heading deviation, θ e ; and bending degree, c , of the look-ahead path as the input variables. Through multiple simulations and adjustments, 75 control rules were developed. The look-ahead distance, Ld , was obtained through fuzzification, fuzzy inference, and defuzzification processes. Next, a speed-control function was constructed based on the agricultural machinery’s pose deviations and the bending degree of the look-ahead path to achieve variable speed control. Finally, field tests were conducted to verify the effectiveness of the proposed path-tracking method. The tracking experiment results for the two types of paths indicate that under the variable-speed dynamic look-ahead distance strategy, the average lateral deviations for the variable-curvature paths were 1.8 cm and 3.3 cm while the maximum lateral deviations were 10.1 cm and 10.5 cm, respectively. Compared to the constant-speed fixed look-ahead pure-pursuit model, the average lateral deviation was reduced by 56.1% and the maximum lateral deviation by 50.4% on the U-shaped path. On the S-shaped path, the average lateral deviation was reduced by 56.0% and the maximum lateral deviation by 58.9%. The proposed method effectively improves the path-tracking accuracy of agricultural machinery on variable-curvature paths, meeting the production requirements for curved operations in agricultural environments.

Suggested Citation

  • Jiawei Zhou & Junhao Wen & Liwen Yao & Zidong Yang & Lijun Xu & Lijian Yao, 2025. "Agricultural Machinery Path Tracking with Varying Curvatures Based on an Improved Pure-Pursuit Method," Agriculture, MDPI, vol. 15(3), pages 1-18, January.
  • Handle: RePEc:gam:jagris:v:15:y:2025:i:3:p:266-:d:1577490
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/15/3/266/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/15/3/266/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Haoling Ren & Jiangdong Wu & Tianliang Lin & Yu Yao & Chang Liu, 2023. "Research on an Intelligent Agricultural Machinery Unmanned Driving System," Agriculture, MDPI, vol. 13(10), pages 1-19, September.
    2. Haoling Ren & Jiangdong Wu & Tianliang Lin & Yu Yao & Chang Liu, 2023. "Correction: Ren et al. Research on an Intelligent Agricultural Machinery Unmanned Driving System. Agriculture 2023, 13 , 1907," Agriculture, MDPI, vol. 14(1), pages 1-8, December.
    3. Wei Liu & Jinhao Zhou & Yutong Liu & Tengfei Zhang & Meng Yan & Ji Chen & Chunjian Zhou & Jianping Hu & Xinxin Chen, 2024. "An Ultrasonic Ridge-Tracking Method Based on Limiter Sliding Window Filter and Fuzzy Pure Pursuit Control for Ridge Transplanter," Agriculture, MDPI, vol. 14(10), pages 1-25, September.
    4. Zejin Chen & Haifeng Wang & Mengchuang Zhou & Jun Zhu & Jiahui Chen & Bin Li, 2024. "Design and Experiment of an Autonomous Navigation System for a Cattle Barn Feed-Pushing Robot Based on UWB Positioning," Agriculture, MDPI, vol. 14(5), pages 1-17, April.
    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. Guannan Lei & Shilong Zhou & Penghui Zhang & Fei Xie & Zihang Gao & Li Shuang & Yanyun Xue & Enjie Fan & Zhenbo Xin, 2025. "Stability Control of the Agricultural Tractor-Trailer System in Saline Alkali Land: A Collaborative Trajectory Planning Approach," Agriculture, MDPI, vol. 15(1), pages 1-19, January.
    3. Ricardo Paul Urvina & César Leonardo Guevara & Juan Pablo Vásconez & Alvaro Javier Prado, 2024. "An Integrated Route and Path Planning Strategy for Skid–Steer Mobile Robots in Assisted Harvesting Tasks with Terrain Traversability Constraints," Agriculture, MDPI, vol. 14(8), pages 1-26, July.
    4. Zejin Chen & Haifeng Wang & Mengchuang Zhou & Jun Zhu & Jiahui Chen & Bin Li, 2024. "Design and Experiment of an Autonomous Navigation System for a Cattle Barn Feed-Pushing Robot Based on UWB Positioning," Agriculture, MDPI, vol. 14(5), pages 1-17, April.
    5. 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.
    6. Yahui Luo & Chen Li & Ping Jiang & Yixin Shi & Bin Li & Wenwu Hu, 2024. "Research on Tractor Condition Recognition Based on Neural Networks," Agriculture, MDPI, vol. 14(4), pages 1-20, April.

    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:3:p:266-:d:1577490. 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.