Topological Gaussian ARTs with Short-Term and Long-Term Memory for Map Building and Fuzzy Motion Planning

This paper proposes a cognitive architecture for building a topological map incrementally inspired by beta oscillations during place cell learning in hippocampus. The proposed architecture consists of two layer: the short-term memory layer and the long-term memory layer. The short-term memory layer emulates the entorhinal and the ? is the orientation system; the long-term memory layer emulates the hippocampus. Nodes in the topological map represent place cells (robot location), links connect nodes and store robot action (i.e. adjacent angle between connected nodes). The proposed method is formed by multiple Gaussian Adaptive Resonance Theory to receive data from various sensors for the map building. It consists of input layer and memory layer. The input layer obtains sensor data and incrementally categorizes the acquired information as topological nodes temporarily (short-term memory). In the long-term memory layer, the categorized information will be associated with robot actions to form the topological map (long-term memory). The advantages of the proposed method are: 1) it is a cognitive model that does not require human defined information and advanced knowledge to implement in a natural environment; 2) it can generate the map by processing various sensors data simultaneously in continuous space that is important for real world implementation; and 3) it is an incremental and unsupervised learning approach. Thus, the authors combine their Topological Gaussian ARTs method (TGARTs) with fuzzy motion planning to constitute a basis for mobile robot navigation in environment with slightly changes. Finally, the proposed approach was verified with several simulations using standardized benchmark datasets and real robot implementation.