Optimizing Urban Sustainability: Reinforcement Learning-Driven Energy-Efficient Ubiquitous Robots for Smart Cities

Abstract

In the rapidly evolving landscape of smart cities, the deployment of ubiquitous robots holds immense potential for enhancing various aspects of urban living. However, the widespread integration of these robots into smart city infrastructures necessitates a careful consideration of energy efficiency to ensure sustainable and long-term operation. By leveraging advanced algorithms, these robots can adapt their behaviors and decision-making processes, leading to reduced energy consumption and increased operational sustainability. This chapter explores the application of reinforcement learning techniques to optimize the energy efficiency of ubiquitous robots operating in smart cities and also investigates various implementation methods of reinforcement learning in the context of smart cities, focusing on enhancing the energy efficiency of ubiquitous robots like search and rescue robots and contributing to the overall development of energy-conscious urban environments.