No abstract

The hippocampus and its accessory are the main areas for spatial cognition. It can integrate paths and form environmental cognition based on motion information and then realize positioning and navigation. Learning from the hippocampus mechanism is a crucial way forward for research in robot perception, so it is crucial to building a calculation method that conforms to the biological principle. In addition, it should be easy to implement on a robot. This paper proposes a bionic cognition model and method for mobile robots, which can realize precise path integration and cognition of space. Our research can provide the basis for the cognition of the environment and autonomous navigation for bionic robots.

This paper proposes a method to realize the robot's self-learning of unknown environment by using a episode cognitive model inspired by the hippocampus. The episode cognitive map constructed through self-learning is suitable for robot navigation in an unknown environment. Which solve the problem of robustness of the robot's perception in complex and dynamic environments. The model we propose is called the hippocampus episode cognitive network (HEC), which is based on the physiological functions of the hippocampus CA1, CA3 and the dentate gyrus combined with the adaptive resonance theory (ART) network. Extract new events through the incremental generation of cognitive neurons, and encode the events into episode through spatio-temporal connections. The episode nodes can be connected to generate a episode cognitive map. This method can realize learning, storage, and update information for autonomous mobility robots in unknown environment. On the basis of 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 positioning and mapping in a complex and dynamic environment.