IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2022i1p56-d1013858.html
   My bibliography  Save this article

Optimal Path Generation with Obstacle Avoidance and Subfield Connection for an Autonomous Tractor

Author

Listed:
  • Tyler Parsons

    (Department of Automotive and Mechatronics Engineering, Ontario Tech University, Oshawa ON L1G 0C5, Canada)

  • Fattah Hanafi Sheikhha

    (Department of Automotive and Mechatronics Engineering, Ontario Tech University, Oshawa ON L1G 0C5, Canada)

  • Omid Ahmadi Khiyavi

    (Department of Automotive and Mechatronics Engineering, Ontario Tech University, Oshawa ON L1G 0C5, Canada)

  • Jaho Seo

    (Department of Automotive and Mechatronics Engineering, Ontario Tech University, Oshawa ON L1G 0C5, Canada)

  • Wongun Kim

    (Convergence Agricultural Machinery Group, Korea Institute of Industrial Technology, Gimje-si 54325, Jeollabuk-do, Republic of Korea)

  • Sangdae Lee

    (Convergence Agricultural Machinery Group, Korea Institute of Industrial Technology, Gimje-si 54325, Jeollabuk-do, Republic of Korea)

Abstract

As autonomous tractors become more common crop harvesting applications, the need to optimize the global servicing path becomes crucial for maximizing efficiency and crop yield. In recent years, several methods of path generation have been researched, but very few have studied their applications on complex field shapes. In this study, a method of creating the optimal servicing path for simple and complex field shapes is proposed. The proposed algorithm creates subfields for a target land, optimizes the track direction for several subfields individually, merges subfields that result in overall increased efficiency, and finds the minimum non-operating paths to travel from subfield to subfield while selecting the respective optimal subfield starting locations. Additionally, it is required that this process must be done within 3 seconds to meet performance requirements. Results from 3 separate field shapes show that the field traversal efficiency can range from 68.0% to 94.4%, and the coverage ratio can range from 98.8% to 99.9% for several different conditions. In comparison with previous studies using the same field shape, the proposed methods demonstrate an increase of 5.5% in field traversal efficiency.

Suggested Citation

  • Tyler Parsons & Fattah Hanafi Sheikhha & Omid Ahmadi Khiyavi & Jaho Seo & Wongun Kim & Sangdae Lee, 2022. "Optimal Path Generation with Obstacle Avoidance and Subfield Connection for an Autonomous Tractor," Agriculture, MDPI, vol. 13(1), pages 1-16, December.
    • Handle: RePEc:gam:jagris:v:13:y:2022:i:1:p:56-:d:1013858
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/1/56/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/1/56/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Suprava Chakraborty & Devaraj Elangovan & Padma Lakshmi Govindarajan & Mohamed F. ELnaggar & Mohammed M. Alrashed & Salah Kamel, 2022. "A Comprehensive Review of Path Planning for Agricultural Ground Robots," Sustainability, MDPI, vol. 14(15), pages 1-19, July.
    2. Mengwei Shen & Suzhen Wang & Shuang Wang & Yan Su, 2020. "Simulation Study on Coverage Path Planning of Autonomous Tasks in Hilly Farmland Based on Energy Consumption Model," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-15, August.
    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. Chuandong Liang & Kui Pan & Mi Zhao & Min Lu, 2023. "Multi-Node Path Planning of Electric Tractor Based on Improved Whale Optimization Algorithm and Ant Colony Algorithm," Agriculture, MDPI, vol. 13(3), pages 1-19, February.

    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. Cristiano Fragassa & Giuliano Vitali & Luis Emmi & Marco Arru, 2023. "A New Procedure for Combining UAV-Based Imagery and Machine Learning in Precision Agriculture," Sustainability, MDPI, vol. 15(2), pages 1-25, January.
    2. Haoming Shi & Fei Xu & Jinfu Cheng & Victor Shi, 2023. "Exploring the Evolution of the Food Chain under Environmental Pollution with Mathematical Modeling and Numerical Simulation," Sustainability, MDPI, vol. 15(13), pages 1-17, June.
    3. Alexander V. Klokov & Egor Yu. Loktionov & Yuri V. Loktionov & Vladimir A. Panchenko & Elizaveta S. Sharaborova, 2023. "A Mini-Review of Current Activities and Future Trends in Agrivoltaics," Energies, MDPI, vol. 16(7), pages 1-18, March.

    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:2022:i:1:p:56-:d:1013858. 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.