Unknown Environment Representation for Mobile Robot Using Spiking Neural Networks

Authors: Amir Reza Saffari Azar Alamdari

Abstract:

In this paper, a model of self-organizing spiking neural networks is introduced and applied to mobile robot environment representation and path planning problem. A network of spike-response-model neurons with a recurrent architecture is used to create robot-s internal representation from surrounding environment. The overall activity of network simulates a self-organizing system with unsupervised learning. A modified A* algorithm is used to find the best path using this internal representation between starting and goal points. This method can be used with good performance for both known and unknown environments.

Keywords: Mobile Robot, Path Planning, Self-organization, Spiking Neural Networks.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1072820

References:

[1] W. Gerstner, W. M. Kistler, Spiking Neuron Model: Single Neuron, Populations, and Plasticity. Cambridge University Press 2002.
[2] W. Maass, "Lower Bounds for the Computational Power of Spiking Neurons" Neural Computation, vol. 8, pp 1-40, 1996.
[3] A. J. Patel "Game Programming: Path Planning", (Online), http://wwwcs- students.stanford.edu/~amitp/gameprog.html#Paths.
[4] Y. Choe, "Perceptual Grouping in a Self-Organizing Map of Spiking Neurons" Ph.D. dissertation, University of Texas at Austin, 2001.
[5] R. Eckhorn, M. Arndt, P. Dike, "Feature Linking via Synchronization Among Distributed Assemblies: Simulation Results from Cat Visual Cortex" Neural Computation, vol. 2, pp 293-307, 1990.
[6] R. Eckhorn, R. Bauer, W. Jordan, M. Brosch, W. Kruse, M. Munk, H. J. Reitboeck, "Coherent Oscillations: A Mechanism of Feature Linking in the Visual Cortex?" Biological Cybernetics, vol. 60, pp 121-130, 1988.
[7] C. W. Eurich, K. Pawelzik, U. Ernst, A. Theil, J. D. Cowan, J. G. Milton "Delay Adaptation in the Nervous System" Neurocomputing, vol. 32-33, pp 741-748, 2000.