Learning performance in evolutionary behavior based mobile robot navigation

In this paper we utilize information theory to study the impact in learning performance of various motivation and environmental configurations. This study is done within the context of an evolutionary fuzzy motivation based approach used for acquiring behaviors in mobile robot exploration of complex environments. Our robot makes use of a neural network to evaluate measurements from its sensors in order to establish its next behavior. Adaptive learning, fuzzy based fitness and Action-based Environment Modeling (AEM) are integrated and applied toward training the robot. Using information theory we determine the conditions that lead the robot toward highly fit behaviors. The research performed also shows that information theory is a useful tool in analyzing robotic training methods.