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Case study on a self-organizing spiking neural network for robot navigation.

Eric Nichols¹, L J McDaid, N H Siddique

¹School of Computing and Intelligent Systems, University of Ulster Derry, BT48 7JL, Northern Ireland. nichols-e1@email.ulster.ac.uk

International Journal of Neural Systems

|December 1, 2010

Summary

This study introduces a Spiking Neural Network (SNN) for robot navigation. The SNN self-organizes its connections and uses working memory to learn and adapt to new environments, demonstrating success in wall-following tasks.

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Area of Science:

Robotics
Artificial Intelligence
Computational Neuroscience

Background:

Mobile robot navigation requires sophisticated control systems.
Spiking Neural Networks (SNNs) offer bio-inspired, event-driven processing for complex tasks.
Existing navigation systems may lack adaptability to novel environmental conditions.

Purpose of the Study:

To propose a novel Spiking Neural Network (SNN) architecture for mobile robot navigation.
To incorporate dynamic synapses and a Leaky Integrate and Fire (LIF) neuron model.
To integrate working memory for enhanced environmental awareness.

Main Methods:

Developed a 4-layer SNN architecture.
Implemented dynamic synapses for information routing.

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Utilized the Leaky Integrate and Fire (LIF) neuron model.

Incorporated a working memory component storing current and past sensor states.

Employed self-organization for learning and connectivity adaptation.

Main Results:

The SNN architecture demonstrated effective learning through self-organization of connectivity.
The integration of working memory enhanced the network's ability to process environmental information.
Successful application in a wall-following navigation task was achieved.

Conclusions:

The proposed SNN architecture provides an adaptive and efficient solution for mobile robot navigation.
Self-organizing connectivity and working memory are key features for environmental learning.
This approach shows promise for robots operating in dynamic and unknown environments.