Cellular Neural Networks-Based Genetic Algorithm for Optimizing the Behavior of an Unstructured Robot

Abstract

A new learning algorithm for advanced robot locomotion is presented in this paper. This method involves both Cellular Neural Networks (CNN) technology and an evolutionary process based on genetic algorithm (GA) for a learning process. Learning is formulated as an optimization problem. CNN Templates are derived by GA after an optimization process. Through these templates the CNN computation platform generates a specific wave leading to the best motion of a walker robot. It is demonstrated that due to the new method presented in this paper an irregular and even a disjointed walker robot can successfully move with the highest performance.