Development and Application of Coverage Control Algorithms: A Concise Review

Coverage control is a foundational domain within multi-agent systems, which has recently undergone substantial advancements. Model-based and optimization-based control methods have achieved new breakthroughs and applications, while data-driven and learning-based algorithms in coverage control have also yielded significant results. This review examines the evolution and application of coverage control algorithms in multi-agent systems. Moreover, this review then focuses on how contemporary algorithms build on traditional control strategies and integrate cutting-edge data-driven and adaptive learning techniques. Finally, it explores potential future directions, emphasizing the importance of interdisciplinary approaches in overcoming existing challenges and seizing new opportunities in coverage control deployment. This review aims to be a valuable resource for researchers and practitioners, guiding continued exploration in this field. Note to Practitioners—This work was motivated by the need to provide an overview of the development and research of existing multi-agent coverage control algorithms. Different application scenarios and constraints require different methods of multi-agent coverage control, each with varying efficiencies. For researchers and practitioners, deciding which coverage control method to use in specific scenarios, while considering practical constraints such as energy limitations, can be challenging. Therefore, this work offers an overview of existing multi-agent coverage control algorithms and suggests potential application scenarios. This review serves as a comprehensive reference for researchers and practitioners in the field, helping them identify suitable solutions to ensure efficient coverage.