Real-time monitoring and diagnosing of robotic assembly with self-organizing neural maps

Abstract

An application of self-organizing neural-network maps is presented for real-time execution monitoring and diagnosing of robotic assembly. The self-organizing maps not only have the ability to spontaneously react to changes in dynamic assembly processes, but also to offer simplicity and feasibility for organizing diverse assembly interactions between tools, parts, robot, and sensory data. A number of different types of multidimensional maps are described for various combinations of assembly interactions.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>