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Proceedings Paper

Dynamics identification for enhanced haptic display in VR-based training platforms
Author(s): D. Bi; You Fu Li; G. L. Wang
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Paper Abstract

Current VR systems mainly use geometric models, which has proved to be insufficient to provide the haptic display capability needed in many applications such as surgery training. Physics based dynamic models play a crucial role in this respect, e.g. for realistic haptic display of the operating feel via Virtual Reality (VR) systems. Such physics based models are desirably obtained via experimental identification. However, traditional dynamics identification methods normally require very large sized training data sets, which maybe difficult to meet in practical applications. This paper presents a method for identifying dynamics models using Support Vector Machines (SVM) regression algorithm which is more effective than traditional methods for high dimensional sparse training data. This method can be used as a generic learning machine or as a special learning technique that can make full use of the known dynamics structure knowledge. The experimental results show the application of our method identifying friction models for realistic haptic display.

Paper Details

Date Published: 1 April 2003
PDF: 8 pages
Proc. SPIE 4756, Third International Conference on Virtual Reality and Its Application in Industry, (1 April 2003); doi: 10.1117/12.497755
Show Author Affiliations
D. Bi, City Univ. of Hong Kong (Hong Kong China)
You Fu Li, City Univ. of Hong Kong (Hong Kong China)
G. L. Wang, City Univ. of Hong Kong (Hong Kong China)

Published in SPIE Proceedings Vol. 4756:
Third International Conference on Virtual Reality and Its Application in Industry
Zhigeng Pan; Jiaoying Shi, Editor(s)

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