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Research on an Intelligent Agricultural Machinery Unmanned Driving System

Author

Listed:
  • Haoling Ren

    (College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China
    Fujian Key Laboratory of Green Intelligent Drive and Transmission for Mobile Machinery, Xiamen 361021, China)

  • Jiangdong Wu

    (College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China
    Fujian Key Laboratory of Green Intelligent Drive and Transmission for Mobile Machinery, Xiamen 361021, China)

  • Tianliang Lin

    (College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China
    Fujian Key Laboratory of Green Intelligent Drive and Transmission for Mobile Machinery, Xiamen 361021, China)

  • Yu Yao

    (Mechatronic Engineering with the School of Beihang University, Beijing 102206, China)

  • Chang Liu

    (College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China
    Fujian Key Laboratory of Green Intelligent Drive and Transmission for Mobile Machinery, Xiamen 361021, China)

Abstract

Intelligent agricultural machinery refers to machinery that can independently complete tasks in the field, which has great significance for the transformation of agricultural modernization. However, most of the existing research on intelligent agricultural machinery is limited to unilateral research on positioning, planning, and control, and has not organically combined the three to form a fully functional intelligent agricultural machinery system. Based on this, this article has developed an intelligent agricultural machinery system that integrates positioning, planning, and control. In response to the problem of large positioning errors in the large range of plane anchoring longitude and latitude, this article integrates geographic factors such as ellipsoid ratio, long and short axis radius, and altitude into coordinate transformation, and combines RTK/INS integrated inertial navigation to achieve precise positioning of the entire vehicle over a large range. In response to the problem that existing full-coverage path planning algorithms only focus on job coverage as the optimization objective and cannot achieve path optimization, this paper proposes a multi-objective function-coupled full-coverage path planning algorithm that integrates three optimization objectives: job coverage, job path length, and job path quantity. This algorithm achieves optimal path planning while ensuring job coverage. As the existing pure pursuit algorithm is not suitable for the motion control of tracked mobile machinery, this paper reconstructs the existing pure pursuit algorithm based on the Kinematics characteristics of tracked mobile machinery, and adds a linear interpolation module, so that the actual tracking path points of motion control are always ideal tracking path points, effectively improving the motion control accuracy and control stability. Finally, the feasibility of the intelligent agricultural machinery system was demonstrated through corresponding simulation and actual vehicle experiments. This intelligent agricultural machinery system can cooperate with various operating tools and independently complete the vast majority of agricultural production activities.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:10:p:1907-:d:1250244
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    References listed on IDEAS

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    1. Mohammad Amiri-Zarandi & Mehdi Hazrati Fard & Samira Yousefinaghani & Mitra Kaviani & Rozita Dara, 2022. "A Platform Approach to Smart Farm Information Processing," Agriculture, MDPI, vol. 12(6), pages 1-18, June.
    2. Changjie Wu & Xiaolong Tang & Xiaoyan Xu, 2023. "System Design, Analysis, and Control of an Intelligent Vehicle for Transportation in Greenhouse," Agriculture, MDPI, vol. 13(5), pages 1-15, May.
    3. Tan Wang & Xianbao Xu & Cong Wang & Zhen Li & Daoliang Li, 2021. "From Smart Farming towards Unmanned Farms: A New Mode of Agricultural Production," Agriculture, MDPI, vol. 11(2), pages 1-26, February.
    4. Shaojiong Huang & Kaoxin Pan & Sibo Wang & Ying Zhu & Qing Zhang & Xin Su & Hongjun Yu, 2023. "Design and Test of an Automatic Navigation Fruit-Picking Platform," Agriculture, MDPI, vol. 13(4), pages 1-25, April.
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    Cited by:

    1. 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.
    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. 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. 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.

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