Evolution of spiking neural circuits in autonomous mobile robots: Research Articles

Authors:
Dario Floreano;Yann Epars;Jean-Christophe Zufferey;Claudio Mattiussi
Affiliations:
Laboratory of Intelligent Systems, Institute of Systems Engineering, Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland;Laboratory of Intelligent Systems, Institute of Systems Engineering, Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland;Laboratory of Intelligent Systems, Institute of Systems Engineering, Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland;Laboratory of Intelligent Systems, Institute of Systems Engineering, Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland
Venue:
International Journal of Intelligent Systems - Intentional Dynamic Systems—Foundations, Modeling, and Robot Implementation
Year:
2006

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Abstract

We describe evolution of spiking neural architectures to control navigation of autonomous mobile robots. Experimental results with simple fitness functions indicate that evolution can rapidly generate spiking circuits capable of navigating in textured environments with simple genetic representations that encode only the presence or absence of synaptic connections. Building on those results, we then describe a low-level implementation of evolutionary spiking circuits in tiny microcontrollers that capitalizes on compact genetic encoding and digital aspects of spiking neurons. The implementation is validated on a sugar-cube robot capable of developing functional spiking circuits for collision-free navigation. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 1005–1024, 2006.