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

Sundaram Manikandan, Ganesan Kaliyaperumal, Saqib Hakak and Thippa Reddy Gadekallu ()
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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
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

Sustainability, 2022, vol. 14, issue 19, 1-24

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.

Keywords: automatic guided vehicle (AGV); model predictive control (MPC); curve detection; navigation; path tracking (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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