Classify Layer Design for Navigation Control of Line-Crawling Robot : A Rough Neurocomputing Approach

This paper considers a rough neurocomputing approach to the design of the classify layer of a Brooks architecture for a robot control system. The Paradigm for neurocomputing that has its roots in rough set theory, and works well in cases where there is uncertainty about the values of measurements used to make decisions. In the case of the line-crawling robot (LCR) described in this paper, rough neurocomputing is used to classify sometimes noisy signals from sensors. The LCR is a robot designed to crawl along high-voltage transmission lines where noisy sensor signals are common because of the electromagnetic field surrounding conductors. In rough neurocomputing, training a network of neurons...