A multi-sensory stimuli computation method for complex robot behavior generation

In this paper we present a method for obstacle avoidance which uses the neural field technique to learn the different actions of the robot. The perception is used based on monocular camera which allows us to have a 2D representation of a scene. Besides, we describe this scene using visual global descriptor called GIST. In order to enhance the quality of the perception, we use laser range data through laser range finder sensor. Having these two observations, GIST and range data, we fuse them using an addition. We show that the fusion data gives better quality when comparing the estimated position of the robot and the ground truth. Since we are using the paradigm learning-test, when the robot acquires data, it uses it as stimuli for the neural field in order to deduce the best action among the four basic ones (right, left, frontward, backward). The navigation is metric so we use Extended Kalman Filter in order to update the robot position using again the combination of GIST and range data.