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System Design, Analysis, and Control of an Intelligent Vehicle for Transportation in Greenhouse

Author

Listed:
  • Changjie Wu

    (Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China)

  • Xiaolong Tang

    (Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China)

  • Xiaoyan Xu

    (Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China)

Abstract

Smart agriculture represents a significant trend in agricultural development, given its potential to enhance operational efficiency and reduce labor intensity. Despite the adoption of modern greenhouse technologies, such as sensors and automation systems, crop transportation is still largely achieved through manual labor, largely due to the complex environment and narrow terrain of greenhouses. To address this challenge, this work proposes the design of an intelligent vehicle that is capable of transporting crops in a commercial greenhouse, with the aim of improving operational efficiency and reducing labor intensity. To enable the vehicle to navigate the horizontal and rail surfaces within the greenhouse, a novel chassis structure is designed that is capable of simultaneous driving on both ground and rail surfaces. Additionally, the two-dimensional codes is adopted for positioning and navigation, thereby avoiding the need to modify existing greenhouse road surfaces. Through the implementation of a comprehensive system-control strategy, the intelligent vehicle realized various functions, including ground driving, rail driving, moving up and down the rail, and automatic rail changing. Experimental results demonstrate that the designed intelligent vehicle successfully meets the basic requirements for crop transportation in a greenhouse, providing a solid foundation for future unmanned operations.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:5:p:1020-:d:1141126
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    References listed on IDEAS

    as
    1. Giorgia Bagagiolo & Giovanni Matranga & Eugenio Cavallo & Niccolò Pampuro, 2022. "Greenhouse Robots: Ultimate Solutions to Improve Automation in Protected Cropping Systems—A Review," Sustainability, MDPI, vol. 14(11), pages 1-17, May.
    2. Long Su & Ruijia Liu & Kenan Liu & Kai Li & Li Liu & Yinggang Shi, 2023. "Greenhouse Tomato Picking Robot Chassis," Agriculture, MDPI, vol. 13(3), pages 1-23, February.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. 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.
    2. 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.

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