Learning actions from vision-based positioning in goal-directed navigation
We describe a navigation approach, based on a reinforcement learning algorithm, that allows a mobile robot to move in an unknown indoor environment learning autonomously in a few trials the actions for reaching a particular goal location. The control architecture merges visual information and sonar readings to evaluate the state of the system to which the learning algorithm relates the best movement for reaching the goal. As a result, after limited experience, the robot learns efficient behavioral sequences and improves its learning incrementally. In addition the learning system adapts well to new situations of the environment or new task requirements. The results obtained in simulation and in real experiments, carried out in our laboratory, have shown that our system is tolerant to noise in sensor measurements and during each trial is always able to reach the goal.