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Design and Testing of a Tractor Automatic Navigation System Based on Dynamic Path Search and a Fuzzy Stanley Model

Author

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  • Bingbo Cui

    (Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Ministry of Education, Zhenjiang 212013, China
    School of Agriculture Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Xinyu Cui

    (Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Ministry of Education, Zhenjiang 212013, China
    School of Agriculture Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Xinhua Wei

    (Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Ministry of Education, Zhenjiang 212013, China
    School of Agriculture Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Yongyun Zhu

    (Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Ministry of Education, Zhenjiang 212013, China
    School of Agriculture Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Zhen Ma

    (Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Ministry of Education, Zhenjiang 212013, China
    School of Agriculture Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Yan Zhao

    (College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China)

  • Yufei Liu

    (College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China)

Abstract

Smart agriculture development mainly depends on the intelligence and reliability of autonomous agricultural machinery. Automatic navigation systems (ANSs) play a key role in intelligent agricultural machinery design, as they not only reduce farmers’ workloads but also improve their land utilization rates. In this paper, a tractor ANS based on dynamic path search and a fuzzy Stanley model (FSM) was designed, and its capability for whole-field path tracking was tested. First, the tracking performance of the steering control module was validated after the automatic reconstruction of the tractor platform. Then, a navigation decision system was established based on a unified reference waypoint search framework, where the path generation for whole-field coverage was presented. Finally, the gain coefficient of the Stanley model (SM) was adjusted adaptively according to the tracking error by utilizing the fuzzy logic controller. Subsequently, the developed tractor ANS was tested in the field. The experiment’s results indicate that the FSM outperformed the SM in straight path tracking and whole-field path tracking. When the tractor traveled at a speed of 1 m/s, the maximum lateral tracking error for the straight path was 10 cm, and the average lateral tracking error was 5.2 cm, showing improvements of 16.7% and 10.3% compared to the SM. Whole-field autonomous navigation showed that the maximum lateral tracking error was improved from 34 cm for the SM to 27 cm for the FSM, a reduction of approximately 20.6%, illustrating the superiority of the FSM in the application of whole-field path tracking. As the maximum tracking error of whole-field autonomous navigation appears in the turning stage, where tractors often stop working, the designed ANS satisfies the requirements of a self-driving system for unmanned tractors.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:12:p:2136-:d:1528901
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    References listed on IDEAS

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    5. Jiamuyang Zhao & Shuxiang Fan & Baohua Zhang & Aichen Wang & Liyuan Zhang & Qingzhen Zhu, 2025. "Research Status and Development Trends of Deep Reinforcement Learning in the Intelligent Transformation of Agricultural Machinery," Agriculture, MDPI, vol. 15(11), pages 1-25, June.

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