Matt Quinn, Lincoln Smith, Giles Mayley, Phil Husbands
In recent years a number of researchers have successfully applied artificial evolution approaches to the design of controllers for autonomous robots. To date, however, Evolutionary Robotics research has focussed almost exclusively on the design of single-robot systems. We are interested in the evolution of controllers for multi-robot systems that are capable of exhibiting cooperative and coordinated behaviour. We report on recent work in which we employed artificial evolution to design neural network controllers for small, homogeneous teams of mobile autonomous robots. The robots are evolved to perform a formation movement task from random starting positions, equipped only with infrared sensors. The dual constraints of homogeneity and minimal sensors make this a non-trivial task. We describe the behaviour of a successful evolved system, in which robots work as a team, adopting and maintaining distinct but interdependent roles in order to achieve the task. We believe this to be the first successful use of evolutionary robotics methodology to develop cooperative, coordinated behaviour for a real multi-robot system.
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