This paper describes a novel approach to robotic haptic recognition, which exploits an unsupervised Kohonen self-organizing feature map for performing a match-to-sample classification of three-dimensional (3-D) objects. The results obtained, even though currently referring to a simulated environment and to some working assumptions, have emphasized the validity of the approach and its applicability in a variety of dextrous robotic systems.

A neural approach to robotich haptic Recognition of 3-D objects based on a Kohonen self-organizing feature map

PASSERI, Daniele;
1997

Abstract

This paper describes a novel approach to robotic haptic recognition, which exploits an unsupervised Kohonen self-organizing feature map for performing a match-to-sample classification of three-dimensional (3-D) objects. The results obtained, even though currently referring to a simulated environment and to some working assumptions, have emphasized the validity of the approach and its applicability in a variety of dextrous robotic systems.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11391/952593
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