A novel attention control modeling method for sensor selection based on fuzzy neural network learning

Abstract

Attention control is one of the best ways to reduce information resources and processing. Discontinuous modeling has been used in attention control and has proven some advantages of attention control. In this paper we present an attention control architecture based on continuous modeling for mobile robot platforms. By using fuzzy neural network we construct efficient attention control which is capable of decreasing sensors sampling rate and also choosing the most efficient set of sensors. We also build a novel method for gathering information to construct fuzzy neural networks. We experimentally proved that fuzzy neural networks are very convenient ways for attention control. By using this method which changes the sampling rate of robot sensors, consumption of energy reduces slightly. This novel framework is implemented on Festo Robotino <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> mobile robot platform and the results show the efficiency of this attention control method which can select the best sensors during each task.