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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.
- Source :
- Sustainability (2071-1050); Oct2022, Vol. 14 Issue 19, p12021-12021, 24p
- Publication Year :
- 2022
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20711050
- Volume :
- 14
- Issue :
- 19
- Database :
- Complementary Index
- Journal :
- Sustainability (2071-1050)
- Publication Type :
- Academic Journal
- Accession number :
- 159700922
- Full Text :
- https://doi.org/10.3390/su141912021