In this article we describe a methodology for evolving neurocontrollers of autonomous mobile robots without human intervention. The presentation, which spans from technological and methodological issues to several experimental results on evolution of physical mobile robots, covers both previous and recent w ork in the attempt to provide a uni ed picture within which the reader can compare the e ects of systematic variations on the experimental settings. After describing some key principles for building mobile robots and tools suitable for experiments in adaptive robotics, we g i v e a n o verview of di erent approaches to evolutionary robotics and present our methodology. We start reviewing two basic experiments showing that di erent e n vironments can shape very di erent behaviors and neural mechanisms under very similar selection criteria. We then address the issue of incremental evolution in two di erent experiments from the perspective of changing environments and robot morphologies. Finally, w e i n vestigate the possibility o f e v olving plastic neurocontrollers and analyze an evolved neurocontroller that relies on fast and continuously changes synapses characterized by dynamic stability. We conclude by reviewing the implications of this methodology for engineering, biology, cognitive science, and arti cial life, and point at future directions of research.
Similar work by REF ] describe a methodology for evolving an Artificial Neural Network-based controller for mobile robots.
Evolutionary Neurocontrollers for Autonomous Mobile Robots
International Conference on Evolutionary Computation
Computer Science Mathematics Medicine