HapticV1, Main, Merge, bibRecord, 007505

Dynamics identification for enhanced haptic display in VR based training platforms

Identifieur interne : 007505 ( Main/Merge ); précédent : 007504; suivant : 007506

Dynamics identification for enhanced haptic display in VR based training platforms

Auteurs : D. Bi [Hong Kong] ; Y. F. Li [Hong Kong] ; G. L. Wang [Hong Kong]

Source :

SPIE proceedings series [ 1017-2653 ] ; 2003.

RBID : Pascal:03-0482265

Descripteurs français

Pascal (Inist)
- Réalité virtuelle, Interface haptique, Modèle physique, Identification dynamique, Affichage haptique, Machine support vecteur.
Wicri :
- topic : Réalité virtuelle.

English descriptors

KwdEn :
- Haptic interfaces, Physical model, Vector support machine, Virtual reality.

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.

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to stream PascalFrancis, to step Corpus: 001137
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Pascal:03-0482265

Le document en format XML

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<front><div type="abstract" xml:lang="en">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.</div>
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Dynamics identification for enhanced haptic display in VR based training platforms

Dynamics identification for enhanced haptic display in VR based training platforms

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri