Emergent Behaviour Evolution in Collective Autonomous Mobile Robots

This paper deals with genetic algorithm based methods for finding optimal structure for a neural network (weights and biases) and for a fuzzy controller (rule set) to control a group of mobile autonomous robots. We have implemented a predator and prey pursuing environment as a test bed for our evolving agents. Using theirs sensorial information and an evolutionary based behaviour decision controller the robots are acting in order to minimize the distance between them and the targets locations. The proposed approach is capable of dealing with changing environments and its effectiveness and efficiency is demonstrated by simulation studies. The goal of the robots, namely catching the targets, could be fulfilled only trough an emergent social behaviour observed in our experimental results. Key-Words: Autonomous mobile robots, emergent behaviour evolution, neural networks, genetic algorithms