Dynamics identification for enhanced haptic display in VR based training platforms
Identifieur interne :
001137 ( PascalFrancis/Corpus );
précédent :
001136;
suivant :
001138
Dynamics identification for enhanced haptic display in VR based training platforms
Auteurs : D. Bi ;
Y. F. Li ;
G. L. WangSource :
-
SPIE proceedings series [ 1017-2653 ] ; 2003.
RBID : Pascal:03-0482265
Descripteurs français
English descriptors
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.
Notice en format standard (ISO 2709)
Pour connaître la documentation sur le format Inist Standard.
| pA |
| A01 | 01 | 1 | | @0 1017-2653 |
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| A05 | | | | @2 4756 |
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| A08 | 01 | 1 | ENG | @1 Dynamics identification for enhanced haptic display in VR based training platforms |
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| A09 | 01 | 1 | ENG | @1 Virtual reality and its application in industry : Hangzhou, 9-12 April 2002 |
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| A11 | 01 | 1 | | @1 BI (D.) |
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| A11 | 02 | 1 | | @1 LI (Y. F.) |
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| A11 | 03 | 1 | | @1 WANG (G. L.) |
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| A12 | 01 | 1 | | @1 ZHIGENG PAN @9 ed. |
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| A12 | 02 | 1 | | @1 JIAOYING SHI @9 ed. |
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| A14 | 01 | | | @1 Department of Manufacturing Engineering and Engineering Management City University of Hong Kong @2 Kowloon @3 HKG @Z 1 aut. @Z 2 aut. @Z 3 aut. |
|---|
| A18 | 01 | 1 | | @1 International Society for Optical Engineering @2 Bellingham WA @3 USA @9 patr. |
|---|
| A20 | | | | @1 129-136 |
|---|
| A21 | | | | @1 2003 |
|---|
| A23 | 01 | | | @0 ENG |
|---|
| A26 | 01 | | | @0 0-8194-4519-3 |
|---|
| A43 | 01 | | | @1 INIST @2 21760 @5 354000117369730170 |
|---|
| A44 | | | | @0 0000 @1 © 2003 INIST-CNRS. All rights reserved. |
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| A45 | | | | @0 14 ref. |
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| A47 | 01 | 1 | | @0 03-0482265 |
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| A60 | | | | @1 P @2 C |
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| A61 | | | | @0 A |
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| A64 | 01 | 1 | | @0 SPIE proceedings series |
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| A66 | 01 | | | @0 USA |
|---|
| C01 | 01 | | ENG | @0 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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| C02 | 01 | X | | @0 001D02C06 |
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| C03 | 01 | X | FRE | @0 Réalité virtuelle @5 01 |
|---|
| C03 | 01 | X | ENG | @0 Virtual reality @5 01 |
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| C03 | 01 | X | SPA | @0 Realidad virtual @5 01 |
|---|
| C03 | 02 | 1 | FRE | @0 Interface haptique @5 02 |
|---|
| C03 | 02 | 1 | ENG | @0 Haptic interfaces @5 02 |
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| C03 | 03 | X | FRE | @0 Modèle physique @5 03 |
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| C03 | 03 | X | ENG | @0 Physical model @5 03 |
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| C03 | 03 | X | SPA | @0 Modelo físico @5 03 |
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| C03 | 04 | X | FRE | @0 Identification dynamique @4 INC @5 82 |
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| C03 | 05 | X | FRE | @0 Affichage haptique @4 INC @5 83 |
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| C03 | 06 | X | FRE | @0 Machine support vecteur @4 CD @5 96 |
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| C03 | 06 | X | ENG | @0 Vector support machine @4 CD @5 96 |
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| N21 | | | | @1 328 |
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| N82 | | | | @1 PSI |
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|
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| pR |
| A30 | 01 | 1 | ENG | @1 International conference on virtual reality and its application in industry @3 Hangzhou CHN @4 2002-04-09 |
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Format Inist (serveur)
| NO : | PASCAL 03-0482265 INIST |
|---|
| ET : | Dynamics identification for enhanced haptic display in VR based training platforms |
|---|
| AU : | BI (D.); LI (Y. F.); WANG (G. L.); ZHIGENG PAN; JIAOYING SHI |
|---|
| AF : | Department of Manufacturing Engineering and Engineering Management City University of Hong Kong/Kowloon/Hong-Kong (1 aut., 2 aut., 3 aut.) |
|---|
| DT : | Publication en série; Congrès; Niveau analytique |
|---|
| SO : | SPIE proceedings series; ISSN 1017-2653; Etats-Unis; Da. 2003; Vol. 4756; Pp. 129-136; Bibl. 14 ref. |
|---|
| LA : | Anglais |
|---|
| EA : | 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. |
|---|
| CC : | 001D02C06 |
|---|
| FD : | Réalité virtuelle; Interface haptique; Modèle physique; Identification dynamique; Affichage haptique; Machine support vecteur |
|---|
| ED : | Virtual reality; Haptic interfaces; Physical model; Vector support machine |
|---|
| SD : | Realidad virtual; Modelo físico |
|---|
| LO : | INIST-21760.354000117369730170 |
|---|
| ID : | 03-0482265 |
|---|
Links to Exploration step
Pascal:03-0482265
Le document en format XML
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