Evolving controllers for a homogeneous system of physical robots: structured cooperation with minimal sensors.
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 were 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 system in which robots adopt and maintain functionally distinct roles in order to achieve the task. We believe this to be the first example of the use of artificial evolution to design coordinated, cooperative behaviour for real robots.
Quinn
Matt
M
Centre for Computational Neuroscience and Robotics, Brighton Innovation Centre, University of Sussex, Brighton BN1 9QG, UK.
Smith
Lincoln
L
Mayley
Giles
G
Husbands
Phil
P
D000222:Adaptation, Physiological / Q000502:physiology; D001185:Artificial Intelligence; D005075:Biological Evolution; D003299:Cooperative Behavior; D004867:Equipment Design / Q000379:methods; D005246:Feedback; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012371:Robotics / Q000295:instrumentation / Q000379:methods*; D014159:Transducers; D014796:Visual Perception / Q000502:physiology*