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

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  • 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
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    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.
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    Cited by:

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