Visual landmark learning

Biology often offers valuable example of systems both for learning and for controlling motion. Work in robotics has often been inspired by these findings in diverse ways. Though the fundamental aspects that involve visual landmark learning and motion control mechanisms have almost exclusively been approached heuristically rather than examining the underlying principles. In this paper we introduce theoretical tools that might explain how the visual learning works and why the motion is attracted by the pre-learnt goal position. Basically, the theoretical tools emerge from the navigation vector field produced by the visual behaviors. Both the learning process and the navigation scheme influence the motion field. We apply classical mathematical and dynamic control to analyze the efficiency of our method