Investigating the Impact of Energy Source Level on the Self-Guided Vehicle System Performances, in the Industry 4.0 Context

Massinissa Graba, Sousso Kelouwani, Lotfi Zeghmi, Ali Amamou, Kodjo Agbossou and Mohammad Mohammadpour
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Massinissa Graba: Research Chair of Noovelia for Intelligent Navigation of Industrial Autonomous Vehicle, Hydrogen Research Institute, Université du Québec à Trois-Rivières, Trois-Rivières, QC G9A 5H7, Canada
Sousso Kelouwani: Research Chair of Noovelia for Intelligent Navigation of Industrial Autonomous Vehicle, Hydrogen Research Institute, Université du Québec à Trois-Rivières, Trois-Rivières, QC G9A 5H7, Canada
Lotfi Zeghmi: Research Chair of Noovelia for Intelligent Navigation of Industrial Autonomous Vehicle, Hydrogen Research Institute, Université du Québec à Trois-Rivières, Trois-Rivières, QC G9A 5H7, Canada
Ali Amamou: Research Chair of Noovelia for Intelligent Navigation of Industrial Autonomous Vehicle, Hydrogen Research Institute, Université du Québec à Trois-Rivières, Trois-Rivières, QC G9A 5H7, Canada
Kodjo Agbossou: Research Chair of Noovelia for Intelligent Navigation of Industrial Autonomous Vehicle, Hydrogen Research Institute, Université du Québec à Trois-Rivières, Trois-Rivières, QC G9A 5H7, Canada
Mohammad Mohammadpour: Research Chair of Noovelia for Intelligent Navigation of Industrial Autonomous Vehicle, Hydrogen Research Institute, Université du Québec à Trois-Rivières, Trois-Rivières, QC G9A 5H7, Canada

Sustainability, 2020, vol. 12, issue 20, 1-21

Abstract: Automated industrial vehicles are taking an imposing place by transforming the industrial operations, and contributing to an efficient in-house transportation of goods. They are expected to bring a variety of benefits towards the Industry 4.0 transition. However, Self-Guided Vehicles (SGVs) are battery-powered, unmanned autonomous vehicles. While the operating durability depends on self-path design, planning energy-efficient paths become crucial. Thus, this paper has no concrete contribution but highlights the lack of energy consideration of SGV-system design in literature by presenting a review of energy-constrained global path planning. Then, an experimental investigation explores the long-term effect of battery level on navigation performance of a single vehicle. This experiment was conducted for several hours, a deviation between the global trajectory and the ground-true path executed by the SGV was observed as the battery depleted. The results show that the mean square error (MSE) increases significantly as the battery’s state-of-charge decreases below a certain value.

Keywords: energy-awareness; industry 4.0; energy resource management; trajectory planning; self-guided vehicle; SGV-system; sustainability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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