Bond Graph based Power Consumption Estimation of a Non-holonomic Wheeled Mobile Robot with Multiple Driving Modes

Bensekrane, Ismail; Singh, Manarshhjot

doi:10.1109/icosc.2018.8587781

Cited by 1 publication

(1 citation statement)

References 9 publications

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“…Hybrid models merge physics-based and data-driven models to estimate energy consumption in robotic systems, capitalizing on both approaches’ strengths for accurate and robust predictions (Lee and Jung, 2015; Dogru and Marques, 2016; Morales and Mendoza, 2018; Kumar et al , 2018). Morales and Mendoza (2018) proposed a hybrid energy model for differentially steered mobile robots, combining dynamic robot and motor models and providing more accurate energy consumption estimates in some aspects.…”

Section: Energy Consumption For Autonomous Mobile Robotsmentioning

confidence: 99%

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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