Mobile Robots' Wall-Following Controller Based on Probabilistic Spiking Neuron Model

Abstract:

This paper focuses on the third generation of neural networks- Spiking neural networks (SNNs), the novel Spiking neuron model- probabilistic Spiking neuron model (pSNM), and their applications. pSNM is used in mobile robots' behavior control, and a novel mobile robots' wall-following controller based on pSNM is proposed. In the pSNM controller, Spiking time-delayed coding is used for the sensory neurons of the input layer and pSNM is used for the motor neurons in the output layer. Thorpe and Hebbian learning rules are used in the controller. The experimental results show that the controller can control the mobile robots to follow the wall clockwise and counterclockwise successfully. The structure of the controller is simple, and the controller can study online.

• Periodical: Advanced Materials Research (Volumes 588-589)
• Pages: 1547-1551
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.588-589.1547
• Online since: November 2012
• Authors: Xiu Qing Wang, Zeng Guang Hou, Min Tan, Yong Ji Wang, Fei Xie
• Keywords: Behavior Control, Mobile Robot, Probabilistic Spiking Neuron Model, Spiking Neural Networks, Wall-Following

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