A case study in vision based neural network training for control of a planar, large deflection, flexible robot manipulator

Abstract

The ability to control large deflection robotic manipulators without a priori dynamic or kinematic mathematical models is desirable on both the macro and micro scales because of the complexity involved in modeling highly flexible manipulators. Neural network control of a particular large deflection manipulator is detailed. The neural network maps the relationship between the motor position and the pixel location of the large deflection, planar, flexible robot manipulator. The neural network is trained using data extracted from images. The network is tested and shown to have small error relative to the range of motion of the finger. The results indicate that visual servoing techniques can be successfully used to train a neural network model for intelligent control of highly flexible manipulators.