An Empirical Evaluation Framework for Autonomous Vacuum Cleaners in Industrial and Commercial Settings: A Multi-Metric Approach

Despite advancements in cleaning automation, there is a noticeable gap in standardized evaluation methods for autonomous vacuum cleaners in industrial and commercial settings. Existing assessments often lack a unified approach, focusing narrowly on either technical capabilities or financial aspects, without integrating both perspectives. This research presents a framework for the evaluation of autonomous vacuum cleaners in industrial and commercial settings, focusing on eight key metrics. These metrics are designed to provide a unified empirical perspective of the vacuum cleaners\u27 performance, operational efficiency, cost, productivity, durability, safety, return on investment, and adaptability. The proposed framework starts with an analysis of cleaning efficiency, examining both the area covered by the cleaners and the quality of cleaning. Advanced image processing techniques are suggested for mapping the area coverage, tailored to different vacuum designs. For assessing cleaning quality, the proposal highlights the potential integration of real-time dirt detection technologies, such as gravimetric sampling and light sensors, to dynamically adapt to varying dirt concentrations and types. Operational efficiency part encompasses the assessment of battery life, charge time, and operational downtime. It advocates for a dual approach of empirical testing and analytical modeling to measure battery life and charge time accurately. The evaluation of operational downtime incorporates tracking of maintenance, charging periods, and other non-operational activities, complemented by predictive modeling for efficient future planning. The financial aspect of the proposed framework encompassed under cost metrics, considers the initial investment, operational and maintenance costs, and potential labor cost savings. This study argues that these cost analysis aids in understanding the long-term financial implications of adopting autonomous vacuum cleaners. Productivity metrics focus on the cleaning speed and the level of autonomy of the vacuum cleaners. Cleaning speed is evaluated using formulas that take into account various environmental factors, while the autonomy level is determined using Sheridan\u27s Levels of Autonomy, which reflects the vacuum\u27s operational independence and its impact on human productivity. Durability, reliability, safety, and compliance are key for vacuum cleaners, evaluated through metrics like Mean Time Between Failures, Mean Time To Repair, Service Life, safety incidents, and adherence to standards and regulations. Lastly, the suggested framework evaluates the vacuum\u27s flexibility and adaptability in different environments, such as various floor types and conditions, highlighting the importance of versatility in autonomous cleaning solutions. Article history: Received: 01/December /2022; Available online: 07/ February/2023; This work is licensed under a Creative Commons International License

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

Battery charge scheduling in long-life autonomous mobile robots via multi-objective decision making under uncertainty

The daily working hours of mobile robots are limited primarily by battery life. Most systems use a combination of thresholds and fixed periods to decide when to charge. This produces charging behaviour that ignores high-value tasks that must be performed within time-windows or by deadlines. Instead the robot should schedule charging adaptively, taking into account the times of day when it is expected to be given more valuable tasks to perform. This paper proposes an approach that exploits the fact that, during long-term deployments, the robot can learn when it is most probable that valuable tasks are added to the system, enabling it to schedule charging at times that are expected to be less busy. We pose the problem of scheduling battery charging as a multi-objective sequential decision making problem over a time-dependent Markov decision process model of expected task rewards and battery dynamics. We evaluate the scalability and solution quality of our multi-objective scheduler, and compare it with a typical rule-based approach. Empirical results show that our approach enables more flexible and efficient robot behaviour, which takes into account both the value of current available tasks and the predicted value of future tasks to decide whether to charge at a given time