Evolutionary behavior learning for action-based environment modeling by a mobile robot

This paper describes an evolutionary way to acquire behaviors of a mobile robot for recognizing environments. We have proposed Action-based Environment Modeling (AEM) approach for a simple mobile robot to recognize environments. In AEM, a behavior-based mobile robot acts in each environment and action sequences are obtained. The action sequences are transformed into vectors characterizing the environments, and the robot identifies the environments with similarity between the vectors. The suitable behaviors like wall-following for AEM have been designed by a human. However the design is very difficult for him/her because the search space is huge and intuitive understanding is hard. Thus we apply evolutionary robotics approach to design of such behaviors using genetic algorithm and make simulations in which a robot recognizes the environments with different structures. As results, we find out suitable behaviors are learned even for environments in which human hardly designs them, and the learned behaviors are more efficient than hand-coded ones.