Evolutionary Swarm Robotics - Task Allocation Strategies

Abstract

Evolutionary Swarm Robotics (ESR) has emerged as a promising field that combines principles from evolutionary computation and swarm robotics to enable robot swarms to perform complex tasks in a decentralized and adaptive manner. Task allocation, a fundamental challenge in swarm robotics, involves assigning tasks to individual robots in a way that optimizes the overall performance of the swarm. In this paper, we focus on studying task allocation strategies in ESR, where robots autonomously allocate tasks based on environmental conditions and team objectives. We review existing approaches and discuss their advantages and limitations. Additionally, we propose a novel task allocation strategy inspired by natural selection and genetic algorithms, which we evaluate through simulations and real-world experiments. Our results demonstrate the effectiveness of the proposed strategy in improving task allocation efficiency and swarm performance in various scenarios. This research contributes to the advancement of ESR by providing insights into effective task allocation strategies that can enhance the scalability, robustness, and adaptability of robot swarms in dynamic environments.