Integration of topological and geometrical planning in a learning mobile robot

The problem of adapting mobile robot navigation to changes in the environment is usually approached by modifying an internal world model. Descriptions on different levels of abstraction provide the information necessary for navigation and therefore influence the robot´s behaviour. The effect of such indirect adaptation is limited. The approach presented in this paper describes a new technique for direct integration of navigation experience in path planning. Thus, not only the world knowledge, but also the planning behaviour is improved over time. Experiments are carried out on a robot which is controlled by a layered architecture. It is integrated in a multirobot control environment which is described. The focus of the article is towards improving the higher navigation levels. The main idea being presented is the realization of adaptive behaviour not only on the level of reflexes, but also with respect to the planning capabilities of the robot. The application of learning techniques allows to continuously improve the estimation of plan costs and therefore the inherent strategy of the topological planner. It is illustrated that a combined learning of world description and navigation allows fast and sophisticated reaction to new environmental conditions