Dealing with robustness in mobile robot guidance while operating with visual strategies

This paper introduces a theory to formally and practically analyze the robustness issues of visual guidance methods for robot navigation. The first aspect is related to the convergence of the navigation system to the goal. It will be shown how the dynamic system which drives the strategies can be analyzed by using classical concepts such as the Liapunov functions. The second aspect concerns the conservativeness of the resulting navigation vector fields. It will be shown how this deals with the repeatability of the trials. Furthermore, the selection of the best landmarks to perform the navigation processes strongly affects the conservativeness thus providing a formal way to do landmark learning. The theory has been tested with two different visual methods that have been derived from the biological world: the snapshot model and the landmark model. The former considers a portion of the full panorama taken by a color camera to accomplish navigating actions. The latter is a more sophisticated approach which uses the most suitable visual landmarks to calculate navigation movements.