A generalised neural network for a humanoid hand

Abstract

The main contribution of this paper is a practical application of generalised neural networks for a dextrous hand moved by shape memory alloys (SMA). Since SMA have highly nonlinear characteristics and their parameters depend on the environment (mainly on temperature) so the robot hand is controlled by a generalised neural network, which can learn the actual nonlinear characteristics of the robot hand. The experimental setup consists of a 20 degree of freedom hand moved by SMA string used as artificial muscle. A video camera is used to detect the position of joints. The position is then sent to the visual display computer via the Internet, which displays the hand in 3D using OpenGl.