Swarm based Optimization Algorithms for Task Allocation in Multi Robot Systems: A Comprehensive Review

Abstract

Multi-robot systems (MRS) have gained significant attention due to their potential applications in various domains such as search and rescue, surveillance, and exploration. An essential aspect of MRS is task allocation, which involves distributing tasks among robots efficiently to achieve collective objectives. Swarm-based optimization algorithms have emerged as effective approaches for task allocation in MRS, leveraging principles inspired by natural swarms to coordinate the actions of multiple robots. This paper provides a comprehensive review of swarm-based optimization algorithms for task allocation in MRS, highlighting their principles, advantages, challenges, and applications. The discussion encompasses key algorithmic approaches, including ant colony optimization, particle swarm optimization, and artificial bee colony optimization, along with recent advancements and future research directions in this field.