Abstract
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.
Highlights
I T is a challenging task for robots to navigate autonomously in a complex and dynamic unknown environment [1] [2]
The Hippocampus Episode Cognition model (HEC) proposed in this paper is inspired by the biological basis of hippocampal neurons, using adaptive resonance theory network (ART) as the basic unit module, combined with our previous research on the spatial cognition model of hippocampal structure. which extracts and encodes sensing and motion information in the space and time dimensions to form episode cognition
The main contribution of this paper is to propose a set of bionic episode cognitive models and robot episode cognitive methods, which can simulate the biological episode memory mechanism and construct a episode cognitive map for robot navigation in uncertain environments
Summary
I T is a challenging task for robots to navigate autonomously in a complex and dynamic unknown environment [1] [2]. This paper proposes a continuous learning, self-adaptive episode cognitive network model, which is used to realize the episode recognition and memory of environmental information by mobile robots. The Hippocampus Episode Cognition model (HEC) proposed in this paper is inspired by the biological basis of hippocampal neurons, using adaptive resonance theory network (ART) as the basic unit module, combined with our previous research on the spatial cognition model of hippocampal structure. The HEC model studied in this paper combines the neural expression results of spatial cognition with visual perception information to form a robot bionic episode cognitive memory. The main contribution of this paper is to propose a set of bionic episode cognitive models and robot episode cognitive methods, which can simulate the biological episode memory mechanism and construct a episode cognitive map for robot navigation in uncertain environments. According to the episode cognitive map, the robot can predict the optimal behavior strategy by activating the episode neurons to adapt to the complex, dynamic, and unknown environment and navigation tasks
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