Evolving Connectionist System Based Role Allocation for Robotic Soccer

Abstract

Robotic soccer is an intelligent system where a group of mobile robots are controlled to perform soccer play ( http://www.fira.net ). The allocation of a suitable role for each robot in a team is a key for the success of the play. The paper treats this issue as one of pattern classification, and solves it with an Evolving classification function (ECF), a special evolving connectionist system (ECOS). A robot's role is determined by and evolves with the states of system (robots and target) in real time. The software and hardware platforms are set up for data collection and learning. The effectiveness of the proposed approach is verified by the experimental studies.