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. The position sensitive detector or PSD, instead of the CCD, is used as a real time vision sensor due to its fast response (The position is converted to analog current). A neural network learns the complex association between the object position and its sensor reading and uses it to track that object. It also turns out that this scheme lends itself to a convenient way to teach a workpath for the robot. Furthermore, for real-time use of the neural net, a novel architecture has been developed based on the concept of input space partitioning and local learning. It exhibits characteristics of fast processing and learning as well as optimal usage of hidden neurons.<>