A NEURAL-NETWORK-BASED REAL-TIME ROBOT TRACKING CONTROLLER USING POSITION-SENSITIVE DETECTORS

Abstract

A real-time visual servo tracking system for an industrial robot has been developed Instead of a charge coupled device (CCD) a position sensitive detector (PSD) is used as the real-time vision sensor due to its fast response (the light position is transduced to analogue current). A neural network learns the complex association between the 3D object position and its sensor reading, and uses it to track that object, either moving or stationary. It also turns out that this scheme lends itself to a user-friendly way to teach workpaths for industrial robots. Furthermore, for real-time use of the neural net, an efficient neural network architecture has been developed based on the concept of input space partitioning and local learning. Real experiments indicate the system's characteristics of fast processing and learning as well as optimal usage of network resources.