Localized qualitative navigation for indoor environments

We describe a novel architecture for indoor navigation, based on qualitative representations of the variations in the interactions between the robot and its environment. We use these representations to localize and guide planning and reaction. The system accepts off-line as input a topological diagram of the environment. It then uses numerical simulation to generate a map, describing qualitative variations in the sensor behavior between adjacent regions in space. An off-line planner stores localized navigation information at each point in the map. During execution, an adaptive controller uses a short-term memory to improve its operation. The qualitative nature of our method, along with the localization performed by the topological planner result in a compact map representation and in linear-time performances for position estimation and path planning during execution. This architecture has been tested in simulation. Our results show that the proposed navigation method is tolerant of sensor inaccuracies, both in obstacle detection and orientation