IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i19p12021-d922730.html
   My bibliography  Save this article

Curve-Aware Model Predictive Control (C-MPC) Trajectory Tracking for Automated Guided Vehicle (AGV) over On-Road, In-Door, and Agricultural-Land

Author

Listed:
  • Sundaram Manikandan

    (TIFAC-CORE Automotive Infotronics, Vellore Institute of Technology (VIT), Vellore 632014, Tamil Nadu, India)

  • Ganesan Kaliyaperumal

    (School of Information Technology and Engineering, Vellore Institute of Technology (VIT), Vellore 632014, Tamil Nadu, India
    Business School, Vellore Institute of Technology (VIT), Vellore 632014, Tamil Nadu, India)

  • Saqib Hakak

    (Faculty of Computer Science, University of New Brunswick, Fredericton, NB E3B 5A3, Canada)

  • Thippa Reddy Gadekallu

    (School of Information Technology and Engineering, Vellore Institute of Technology (VIT), Vellore 632014, Tamil Nadu, India)

Abstract

Navigating the AGV over the curve path is a difficult problem in all types of navigation (landmark, behavior, vision, and GPS). A single path tracking algorithm is required to navigate the AGV in a mixed environment that includes indoor, on-road, and agricultural terrain. In this paper, two types of proposed methods are presented. First, the curvature information from the generated trajectory (path) data is extracted. Second, the improved curve-aware MPC (C-MPC) algorithm navigates AGV in a mixed environment. The results of the real-time experiments demonstrated that the proposed curve finding algorithm successfully extracted curves from all types of terrain (indoor, on-road, and agricultural-land) path data with low type 1 (percentage of the unidentified curve) and type 2 (extra waypoints added to identified curve) errors, and eliminated path noise (hand-drawn line error over map). The AGV was navigated using C-MPC, and the real-time and simulation results reveal that the proposed path tracking technique for the mixed environment (indoor, on-road, agricultural-land, and agricultural-land with slippery error) successfully navigated the AGV and had a lower RMSE lateral and longitudinal error than the existing path tracking algorithm.

Suggested Citation

  • Sundaram Manikandan & Ganesan Kaliyaperumal & Saqib Hakak & Thippa Reddy Gadekallu, 2022. "Curve-Aware Model Predictive Control (C-MPC) Trajectory Tracking for Automated Guided Vehicle (AGV) over On-Road, In-Door, and Agricultural-Land," Sustainability, MDPI, vol. 14(19), pages 1-24, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12021-:d:922730
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/19/12021/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/19/12021/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Maxim A. Dulebenets, 2018. "A Diploid Evolutionary Algorithm for Sustainable Truck Scheduling at a Cross-Docking Facility," Sustainability, MDPI, vol. 10(5), pages 1-23, April.
    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. Monica Dutta & Deepali Gupta & Yasir Javed & Khalid Mohiuddin & Sapna Juneja & Zafar Iqbal Khan & Ali Nauman, 2023. "Monitoring Root and Shoot Characteristics for the Sustainable Growth of Barley Using an IoT-Enabled Hydroponic System and AquaCrop Simulator," Sustainability, MDPI, vol. 15(5), pages 1-17, March.

    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. Riya Kakkar & Rajesh Gupta & Smita Agrawal & Sudeep Tanwar & Ahmed Altameem & Torki Altameem & Ravi Sharma & Florin-Emilian Turcanu & Maria Simona Raboaca, 2022. "Blockchain and IoT-Driven Optimized Consensus Mechanism for Electric Vehicle Scheduling at Charging Stations," Sustainability, MDPI, vol. 14(19), pages 1-20, October.
    2. Oluwatosin Theophilus & Maxim A. Dulebenets & Junayed Pasha & Olumide F. Abioye & Masoud Kavoosi, 2019. "Truck Scheduling at Cross-Docking Terminals: A Follow-Up State-Of-The-Art Review," Sustainability, MDPI, vol. 11(19), pages 1-23, September.
    3. Dulebenets, Maxim A., 2019. "A Delayed Start Parallel Evolutionary Algorithm for just-in-time truck scheduling at a cross-docking facility," International Journal of Production Economics, Elsevier, vol. 212(C), pages 236-258.
    4. Oleksandra Osypchuk & Katarzyna Sosik, 2021. "Impact of the Construction Supplies Implementation on Road Safety in the City Center: A Case Study of the City of Szczecin," Sustainability, MDPI, vol. 13(4), pages 1-15, February.
    5. Rocío González-Sánchez & Davide Settembre-Blundo & Anna Maria Ferrari & Fernando E. García-Muiña, 2020. "Main Dimensions in the Building of the Circular Supply Chain: A Literature Review," Sustainability, MDPI, vol. 12(6), pages 1-25, March.
    6. Haili Zhang & Michael Song & Xiaoming Yang & Ping Li, 2019. "What are Important Technologies for Sustainable Development in the Trucking Industries of Emerging Markets? Differences between Organizational and Individual Buyers," Sustainability, MDPI, vol. 12(1), pages 1-23, December.
    7. Reza Kiani Mavi & Mark Goh & Neda Kiani Mavi & Ferry Jie & Kerry Brown & Sharon Biermann & Ahmad A. Khanfar, 2020. "Cross-Docking: A Systematic Literature Review," Sustainability, MDPI, vol. 12(11), pages 1-19, June.
    8. Auwal Alhassan Musa & Salim Idris Malami & Fayez Alanazi & Wassef Ounaies & Mohammed Alshammari & Sadi Ibrahim Haruna, 2023. "Sustainable Traffic Management for Smart Cities Using Internet-of-Things-Oriented Intelligent Transportation Systems (ITS): Challenges and Recommendations," Sustainability, MDPI, vol. 15(13), pages 1-15, June.
    9. Tarik Chargui & Abdelghani Bekrar & Mohamed Reghioui & Damien Trentesaux, 2019. "Multi-Objective Sustainable Truck Scheduling in a Rail–Road Physical Internet Cross-Docking Hub Considering Energy Consumption," Sustainability, MDPI, vol. 11(11), pages 1-23, June.
    10. Masoud Kavoosi & Maxim A. Dulebenets & Junayed Pasha & Olumide F. Abioye & Ren Moses & John Sobanjo & Eren E. Ozguven, 2020. "Development of Algorithms for Effective Resource Allocation among Highway–Rail Grade Crossings: A Case Study for the State of Florida," Energies, MDPI, vol. 13(6), pages 1-28, 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:jsusta:v:14:y:2022:i:19:p:12021-:d:922730. 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.