Scalability in Evolved Neurocontrollers That Guide a Swarm of Robots in a Navigation Task

Generally speaking, the behavioural strategies of a multirobot system can be defined as scalable if the performance of the system does not drop by increasing the cardinality of the group. The research work presented in this paper studies the issue of scalability in artificial neural network controllers designed by evolutionary algorithms. The networks are evolved to control homogeneous group of autonomous robots required to solve a navigation task in an open arena. This work shows that, the controllers designed to solve the task, generate navigation strategies which are potentially scalable. However, through an analysis of the dynamics of the single robot controller we identify elements that significantly hinder the scalability of the system. The analysis we present in this paper helps to understand the principles underlying the concepts of scalability in this kind of multi-robot systems and to design more scalable solutions.