A Connectionist model for localization and route learning based on remembrance of perception and action

This paper proposes a connectionist model to learn a spatial representation of the world based on temporal memory of perceptions and actions of the robot. It is constructed at run-time to merge the experiences and retrieved in later runs to guide the robot to perform the navigation task. A coding strategy is introduced to extract the directional information from the perception sequence, which endows the robot with localization ability. The Temporal Sequence Processing Network (TSPN) transforms routing knowledge learned from robots experiences into temporal characteristics of cell firing and enables the implicit building of a metric map. The navigation system integrating TSPN and a reactive safeguard module performs collision-free navigation, dynamic landmark and heading detection, route learning and path planning in a noisy world, which is tolerant of sensor inaccuracies and unexpected obstacles. The simulation and real world experiments demonstrate the flexibility and robustness of the system.