Energy Efficient Order Picking Routing for a Pick Support Automated Guided Vehicle (Ps-AGV)

Mejri, Emna; Kélouwani, Sousso; Dubé, Yves; Henao, Nilson; Agbossou, Kodjo

doi:10.1109/access.2022.3212797

Cited by 8 publications

(2 citation statements)

References 61 publications

(78 reference statements)

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“…These algorithms can generally be divided into two main categories: algorithm types and optimization directions. Algorithm types include: the Dijkstra algorithm (Zhang et al , 2021; Emna et al , 2022); the A* algorithm (Liu and Sun, 2011; Jaroszek and Trojnacki, 2014; Liu and Sun, 2014; Sakayori and Ishigami, 2017; Liu et al , 2020; Liu et al , 2021; Shukla and Kumar, 2022); The optimal control theory (Kim and Kim, 2012; Kim and Kim, 2014) ; Tabu-search (Wei et al , 2012); the genetic algorithm (GA); ant colony optimization algorithm (Wongwirat and Anuntachai, 2011; Anuntachai et al , 2014); bee swarm optimization (BSO); PSO; cuckoo–beetle swarm search (CBSS) algorithm (Dechao et al , 2022); and deep reinforcement learning algorithm (Nguyen et al , 2020). …”

Section: Energy Optimization For Autonomous Mobile Robotsmentioning

confidence: 99%

Section: Energy Optimization For Autonomous Mobile Robot In Path Plan...mentioning

confidence: 99%

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A review on energy efficiency in autonomous mobile robots

Wu,

Yeong,

et al. 2023

RIA

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Purpose This paper aims to provide a comprehensive analysis of the state of the art in energy efficiency for autonomous mobile robots (AMRs), focusing on energy sources, consumption models, energy-efficient locomotion, hardware energy consumption, optimization in path planning and scheduling methods, and to suggest future research directions. Design/methodology/approach The systematic literature review (SLR) identified 244 papers for analysis. Research articles published from 2010 onwards were searched in databases including Google Scholar, ScienceDirect and Scopus using keywords and search criteria related to energy and power management in various robotic systems. Findings The review highlights the following key findings: batteries are the primary energy source for AMRs, with advances in battery management systems enhancing efficiency; hybrid models offer superior accuracy and robustness; locomotion contributes over 50% of a mobile robot’s total energy consumption, emphasizing the need for optimized control methods; factors such as the center of mass impact AMR energy consumption; path planning algorithms and scheduling methods are essential for energy optimization, with algorithm choice depending on specific requirements and constraints. Research limitations/implications The review concentrates on wheeled robots, excluding walking ones. Future work should improve consumption models, explore optimization methods, examine artificial intelligence/machine learning roles and assess energy efficiency trade-offs. Originality/value This paper provides a comprehensive analysis of energy efficiency in AMRs, highlighting the key findings from the SLR and suggests future research directions for further advancements in this field.

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