A hybrid architecture for autonomous navigation using a CBR reactive layer

This paper presents a hybrid architecture for autonomous robot navigation. It includes a deliberative layer that hierarchically extracts a global path from a geometrical-topological model of the environment. This path is decomposed into a set of partial goals. Then, a new case-based reasoning based reactive layer capable of learning new local navigation strategies moves from each partial goal to the next. The architecture has been successfully tested in real dynamic environments.