Visual state recognition for a target-reaching task

We present a learning algorithm that realizes a simple goal-reaching task for an autonomous vehicle when only visual information about the goal is provided. The robot has to reach a door from every position of the environment. The state of the system is based on visual information received by a TV camera placed on the mobile robot. The vision algorithm is able to determine the relative position between the vehicle and the door according to the slopes of the contour lines of the door. A learning phase is carried out in simulation to obtain the optimal state-action rules. The learned knowledge is then transferred on the real robot for the testing phase. Experimental results show the generality of the knowledge learned as the real robot is always able to execute its paths towards the door in different environments