IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i10p1907-d1250244.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/10/1907/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/10/1907/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    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. Martina Šestak & Daniel Copot, 2023. "Towards Trusted Data Sharing and Exchange in Agro-Food Supply Chains: Design Principles for Agricultural Data Spaces," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
    2. Mohammad Amiri-Zarandi & Rozita A. Dara & Emily Duncan & Evan D. G. Fraser, 2022. "Big Data Privacy in Smart Farming: A Review," Sustainability, MDPI, vol. 14(15), pages 1-18, July.
    3. Juan D. Borrero & Jesús Mariscal, 2022. "A Case Study of a Digital Data Platform for the Agricultural Sector: A Valuable Decision Support System for Small Farmers," Agriculture, MDPI, vol. 12(6), pages 1-15, May.
    4. Zhikai Ma & Kun Chong & Shiwei Ma & Weiqiang Fu & Yanxin Yin & Helong Yu & Chunjiang Zhao, 2022. "Control Strategy of Grain Truck Following Operation Considering Variable Loads and Control Delay," Agriculture, MDPI, vol. 12(10), pages 1-14, September.
    5. Bin Zhang & Xuegeng Chen & Huiming Zhang & Congju Shen & Wei Fu, 2022. "Design and Performance Test of a Jujube Pruning Manipulator," Agriculture, MDPI, vol. 12(4), pages 1-21, April.
    6. Yehong Liu & Xin Wang & Dong Dai & Can Tang & Xu Mao & Du Chen & Yawei Zhang & Shumao Wang, 2023. "Knowledge Discovery and Diagnosis Using Temporal-Association-Rule-Mining-Based Approach for Threshing Cylinder Blockage," Agriculture, MDPI, vol. 13(7), pages 1-21, June.
    7. Akshat Jain & Prateek Jain, 2022. "Advances in Sustainable Agri Business Paradigm: Developing an Innovative Business and Marketing Model to abridge human labour predicting Neural Behaviour," The Indian Journal of Labour Economics, Springer;The Indian Society of Labour Economics (ISLE), vol. 65(4), pages 1193-1208, December.
    8. Jinying Li & Ananda Maiti & Jiangang Fei, 2023. "Features and Scope of Regulatory Technologies: Challenges and Opportunities with Industrial Internet of Things," Future Internet, MDPI, vol. 15(8), pages 1-27, July.
    9. Guangxiu Ning & Lide Su & Yong Zhang & Jian Wang & Caili Gong & Yu Zhou, 2023. "Research on TD3-Based Distributed Micro-Tillage Traction Bottom Control Strategy," Agriculture, MDPI, vol. 13(6), pages 1-17, June.

    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:13:y:2023:i:10:p:1907-:d:1250244. 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.