Task Allocation Strategies in Multi-robot Systems: Exploring task allocation strategies for optimizing the assignment of tasks among robots in multi-robot systems

Abstract

Multi-robot systems (MRS) are increasingly utilized in various fields, including search and rescue, surveillance, and industrial automation, due to their potential for improved efficiency and flexibility compared to single-robot systems. An essential aspect of MRS is task allocation, which involves assigning tasks to robots in a way that optimizes system performance. This paper presents a comprehensive review of task allocation strategies in MRS, focusing on recent developments and challenges in the field. We discuss the key factors influencing task allocation, such as robot capabilities, task characteristics, communication constraints, and environmental conditions. We categorize existing task allocation strategies based on their underlying principles, including market-based approaches, swarm intelligence techniques, and optimization algorithms. We also highlight the importance of considering uncertainty and dynamic changes in the environment when designing task allocation strategies. Finally, we identify future research directions to address the remaining challenges and further enhance the efficiency and adaptability of task allocation in MRS.