Autonomous task allocation by artificial evolution for robotic swarms in complex tasks

摘要

Swarm robotics is a field in which multiple robots coordinate their collective behavior autonomously to accomplish a given task without any form of centralized control. In swarm robotics, task allocation refers to the behavior resulting in robots being dynamically distributed over different sub-tasks, which is often required for solving complex tasks. It has been well recognized that evolutionary robotics is a promising approach to the development of collective behaviors for robotic swarms. However, the artificial evolution often suffers from two issues-the bootstrapping problem and deception-especially when the underlying task is profoundly complex. In this study, we propose a two-step scheme consisting of task partitioning and autonomous task allocation to overcome these difficulties. We conduct computer simulation experiments where robotic swarms have to accomplish a complex collective foraging problem, and the results show that the proposed approach leads to perform more effectively than a conventional evolutionary robotics approach.