A behavior controller based on spiking neural networks for mobile robots

Spiking neural networks (SNNs), as the third generation of artificial neural networks, have unique advantages and are good candidates for robot controllers. A behavior controller based on a spiking neural network is designed for mobile robots to avoid obstacles using ultrasonic sensory signals. Detailed structure and implementation of the controller are discussed. In the controller the integrated-and-firing model is used and the SNN is trained by the Hebbian learning algorithm. Under the framework of SNNs, fewer neurons are employed in the controller than those of the classical neural networks (NNs). Experimental results show that the proposed controller is effective and is easy to implement. (c) 2007 Elsevier B.V. All rights reserved.