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3D tactile sensor array processed by CNN‐UM: a fast method for detecting and identifying slippage and twisting motion
Author(s) -
Kis Attila,
Kovács Ferenc,
Szolgay Péter
Publication year - 2006
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/cta.364
Subject(s) - slippage , tactile sensor , computer science , artificial intelligence , computer vision , controller (irrigation) , motion (physics) , robot , artificial neural network , engineering , agronomy , structural engineering , biology
In this paper, we present a fast and efficient technique for detecting and identifying the slippage and twisting motion of touching objects. This kind of action cannot be detected with tactile sensors sensing only the normal (perpendicular) component of the forces acting between surfaces. Our approach utilizes an integrated sensing–processing–actuating system comprising: (1) A 2 × 2 taxel (tactile pixel) array mounted on a two‐fingered robot hand, (2) a 64 × 64 CNN‐UM (Cellular Neural Network‐Universal machine), and (3) a closed‐loop controller. This arrangement, along with the proper analogic algorithm, allows detection and the control of the tactile event. It is essential to know and comprehend the forces between contact surfaces and the related 3D pressure fields is essential in many robotic applications discussed in the paper. Copyright © 2006 John Wiley & Sons, Ltd.