Method of Robot Episode Cognition Based on Hippocampus Mechanism

This paper proposes a method to realize the robot’s self-learning of environment by using an episode cognition model inspired by the hippocampus. The episode cognition map is suitable for robot navigation in an unknown environment, which solves the problem of robustness of the robot’s perception in complex and dynamic environments. The model called the hippocampus episode cognitive network (HEC), is based on the hippocampus CA1, CA3, and the dentate gyrus, and combined with the adaptive resonance theory (ART) network. It extract new events through the incremental generation of cognitive neurons, and encode the events into episodes through space-time connections. The episode nodes can be connected to generate an episode cognition map. This method can learn, store, and update information for autonomous mobile robots in an unknown environment. Based on the episode cognitive map, the path trajectory can be predicted through the playback of the episode neuron. The experimental results on the mobile robot show that this method can effectively improve the robot’s adaptability for location and mapping in a complex and dynamic environment.