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 non-linear 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 non-linear 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.
Highlights
Telemanipulation present an process where the operator has some task done at the far environment where he/she cannot be present physically
One process is the interaction between the operator and the master device, the other is the interaction between the slave device and the far environment contact
The master device represents the far environment at the operator site, and the slave device represents the operator at the remote site
Summary
Telemanipulation present an process where the operator has some task done at the far environment where he/she cannot be present physically. The telemanipulation can be well utilised in dangerous or otherwise unreachable environment Human operators perform their tasks mostly by hand, so the master and slave devices must fit the operator’s hand. The paper [7] proposed a sensor glove with force feedback to the all 20 joints of the operator’s hand. This paper proposes a hand type slave device for precise telemanipulation. Until this point no force feedback has been implemented in this system, the actual set. The positions of antecedent sets determine the sampling points of the approximated function.
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