Integrating Big Data and Machine Learning to Improve the Decision-Making Processes of Autonomous Robotic Cleaners

Abstract

The advent of autonomous robotic cleaners has significantly enhanced operational efficiency in various cleaning domains, from household environments to industrial settings. However, their decision-making processes often lack the adaptability and intelligence required for dynamic and complex environments. This paper explores the integration of big data analytics and machine learning (ML) techniques to improve the decision-making capabilities of autonomous robotic cleaners. By analyzing vast datasets collected from sensors and external sources, and applying machine learning algorithms, these robots can learn from their environment, predict potential obstacles, and optimize cleaning routes in real-time. The study demonstrates how such integration can lead to more efficient, effective, and intelligent cleaning solutions, significantly reducing human intervention while adapting to changing environmental conditions. Through experimental setups and simulations, we validate the proposed approach, showcasing notable improvements in decision-making accuracy, cleaning efficiency, and adaptability to unforeseen situations.