Different learning methodologies for vision-based navigation behaviors

In this work the complex behavior of localizing a mobile vehicle with respect to the door of the environment and then reaching the door has been developed. The robot uses visual information to detect and recognize the door and to determine its state with respect to it. This complex task has been divided into two separate behaviors: door-recognition and door-reaching. A supervised methodology based on learning by components has been applied for recognizing the door. Learning by components allows to recognize the door also in difficult situations such as partial occlusions and besides, it makes recognition independent of viewpoint variations and scale changes. An unsupervised methodology based on reinforcement learning has been used for the door-reaching behavior, instead. The image of the door gives information about the relative position of the vehicle with respect to the door. Then the Q-learning algorithm is used to generate the optimal state-action associations. The problem of defining the state and the action sets has been addressed with the aim of producing smooth paths, of reducing the effects of visual errors during real navigation, and of keeping low the computational cost during the learning phase. A novel way to obtain a continuous action set has been introduced: it uses a fuzzy model to evaluate the system state. Experimental results in real environment show both the robustness of the door-recognition behavior and the generality of the door-reaching behavior.