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A Comprehensive Overview of Control Algorithms, Sensors, Actuators, and Communication Tools of Autonomous All-Terrain Vehicles in Agriculture

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  • Hamed Etezadi

    (Department of Bioresource Engineering, McGill University, Montréal, QC H9X 3V9, Canada)

  • Sulaymon Eshkabilov

    (Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USA)

Abstract

This review paper discusses the development trends of agricultural autonomous all-terrain vehicles (AATVs) from four cornerstones, such as (1) control strategy and algorithms, (2) sensors, (3) data communication tools and systems, and (4) controllers and actuators, based on 221 papers published in peer-reviewed journals for 1960–2023. The paper highlights a comparative analysis of commonly employed control methods and algorithms by highlighting their advantages and disadvantages. It gives comparative analyses of sensors, data communication tools, actuators, and hardware-embedded controllers. In recent years, many novel developments in AATVs have been made due to advancements in wireless and remote communication, high-speed data processors, sensors, computer vision, and broader applications of AI tools. Technical advancements in fully autonomous control of AATVs remain limited, requiring research into accurate estimation of terrain mechanics, identifying uncertainties, and making fast and accurate decisions, as well as utilizing wireless communication and edge cloud computing. Furthermore, most of the developments are at the research level and have many practical limitations due to terrain and weather conditions.

Suggested Citation

  • Hamed Etezadi & Sulaymon Eshkabilov, 2024. "A Comprehensive Overview of Control Algorithms, Sensors, Actuators, and Communication Tools of Autonomous All-Terrain Vehicles in Agriculture," Agriculture, MDPI, vol. 14(2), pages 1-42, January.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:2:p:163-:d:1324325
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    References listed on IDEAS

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    1. En Lu & Jialin Xue & Tiaotiao Chen & Song Jiang, 2023. "Robust Trajectory Tracking Control of an Autonomous Tractor-Trailer Considering Model Parameter Uncertainties and Disturbances," Agriculture, MDPI, vol. 13(4), pages 1-17, April.
    2. Farinaz Behrooz & Norman Mariun & Mohammad Hamiruce Marhaban & Mohd Amran Mohd Radzi & Abdul Rahman Ramli, 2018. "Review of Control Techniques for HVAC Systems—Nonlinearity Approaches Based on Fuzzy Cognitive Maps," Energies, MDPI, vol. 11(3), pages 1-41, February.
    3. Chen Jie & Guo Yanling, 2021. "Research on Control Strategy of the Electric Power Steering System for All-Terrain Vehicles Based on Model Predictive Current Control," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-15, March.
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    Cited by:

    1. Shaocen Zhang & Qingshan Liu & Haihui Xu & Zhang Yang & Xinyu Hu & Qi Song & Xinhua Wei, 2025. "Path Tracking Control of a Large Rear-Wheel–Steered Combine Harvester Using Feedforward PID and Look-Ahead Ackermann Algorithms," Agriculture, MDPI, vol. 15(7), pages 1-23, March.

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