Skip to Main Content
This article presents a bio-inspired neural network providing planning capabilities in autonomous navigation applications. The proposed architecture (hippocampus model) learns, recognizes and predicts transitions between places for any system able to provide a localization gradient from the current position to each learned place. The recurrent synapses of a cognitive map (prefrontal cortices model) encoded the spatio-temporal connectivity of the performed transitions. Particular transitions of interest (goal transitions) are associated to the satisfaction of drives. While planning, the diffusion of an activity from the goal transitions in the cognitive map allows to compute a proximity gradient to the goal from each learned transition. The shortest plan of transition to reach the goal is computed by merging the cognitive map information and the prediction of the possible transitions (nucleus accumbens). In parallel, a sensory-motor learning between the performed transitions and the corresponding movements occurs and enables to physically execute the proposed plan (cerebellum). Refinements (active forgetting capabilities) are proposed for the cognitive map building. The whole system is experimented on a real robot which autonomously learns a stable representation of its environment during a long random walk and proves to be able to return to the goal from any position of the environment.