A review on energy efficiency in autonomous mobile robots

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

Optimization of Automated Guided Vehicles (AGV) Fleet Size With Incorporation of Battery Management

An important aspect in manufacturing automation is material handling. To facilitate material handling, automated transport systems are implemented and employed. The AGV (automated guided vehicle) has become widely used for internal and external transport of materials. A critical aspect in the use of AGVs is determining the number of vehicles required for the system to meet the material handling requirements. Several models and simulations have been applied to determine the fleet size. Most of these models and simulations do not incorporate the battery usage of the vehicles and the effect it can have on the throughput and the number of AGVs required for the system. The goal of this research is to develop a simulation model to determine the optimized number of AGVs that is capable of increasing throughput while meeting the material handling requirements of the system. This model incorporates the battery management aspect and issues, which are usually omitted in AGV research. This includes the charging options and strategies, the number and location of charging stations, maintenance, and extended charging. The analysis entails studying various scenarios by applying different charging options and strategies and changing different parameters to achieve improved throughput and an optimized AGV fleet size. The results clearly show that battery management can have a significant effect on the average throughput and the AGV usage. It is important that the battery management of the AGVs is addressed adequately to run an AGV system efficiently