Robotic snake simulation using ensembles of artificial neural networks in evolutionary robotics

Abstract

The Evolutionary Robotics process requires the evaluation of large numbers of robot controllers in order to determine their relative fitnesses. The evaluation of many controllers is typically performed in simulation instead of real-world hardware in order to speed up the evolutionary process and avoid damage to robot hardware. Physics-based simulators are traditionally used in robotics for evaluating controllers. Effective traditional simulators may require a high level of accuracy and their creation requires specialised knowledge of the dynamics of the robotic system. Alternatively Artificial Neural Network based simulators are relatively simple to construct, are highly accurate, efficient and assume little specialised knowledge of the dynamics involved.