NDLI: Biologically Inspired Topological Gaussian ARAM for Robot Navigation

Content Provider	IEEE Xplore Digital Library
Author	Wei Hong Chin Chu Kiong Loo
Copyright Year	2013
Description	Author affiliation: Fac. of Comput. Sci. & Inf. Technol., Univ. of Malaya, Kuala Lumpur, Malaysia (Wei Hong Chin; Chu Kiong Loo)
Abstract	This paper presents a neural network for online topological map construction inspired by the beta oscillations and hippocampal place cell learning. In our proposed method, nodes in the topological map represent place cells (robot location) while edges connect nodes and store robot action (i.e. orientation, direction). Our proposed method (TGARAM) comprises 2 layers: the input layer and the memory layer. The input layer collects sensory information and cluster the obtained information into a set of topological nodes incrementally. In the memory layer, the clustered information is used as a topological map where nodes are associated with actions. Then, topological nodes are clustered together into space regions to represent the environment in the memory layer. The advantages of the proposed method are that 1) it does not require high-level cognitive processes and prior knowledge which is able to work in natural environment, 2) it can process multiple sensory sources simultaneously in continuous space, and 3) it is an incremental and unsupervised learning method. Thus, topological map generated by TGARAM is utilised for path planning to constitutes a basis for robot navigation. Finally, we validate the proposed method through several experiments.
Starting Page	265
Ending Page	269
File Size	322599
Page Count	5
File Format	PDF
e-ISBN	9781479931842
DOI	10.1109/RVSP.2013.66
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-10
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Topological Map Navigation Subspace constraints Robot sensing systems Path planning Adaptive Resonance Theory Place Cell Learning Hippocampus Unsupervised learning
Content Type	Text
Resource Type	Article

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