Haptic deformation modelling through cellular neural network
This paper presents a new methodology for the deformation of soft objects by drawing an analogy between cellular neural network (CNN) and elastic deformation. The potential energy stored in an elastic body as a result of a deformation caused by an external force is propagated among mass points by no...
| Main Authors: | , , , |
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| Format: | Journal Article |
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UK Society for Modelling and Simulation
2006
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| Online Access: | http://ijssst.info/Vol-07/No-8/cover.htm http://hdl.handle.net/20.500.11937/7936 |
| _version_ | 1848745512820277248 |
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| author | Zhong, Yongmin Shirinzadeh, B. Alici, G. Smith, J. |
| author_facet | Zhong, Yongmin Shirinzadeh, B. Alici, G. Smith, J. |
| author_sort | Zhong, Yongmin |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper presents a new methodology for the deformation of soft objects by drawing an analogy between cellular neural network (CNN) and elastic deformation. The potential energy stored in an elastic body as a result of a deformation caused by an external force is propagated among mass points by nonlinear CNN activities. The novelty of the methodology is that soft object deformation is carried out from the perspective of energy propagation, and nonlinear material properties are modelled with nonlinear CNNs, rather than geometric nonlinearity as in most of the existing deformation methods. Integration with a haptic device has been achieved to simulate soft object deformation with force feedback. The proposed methodology not only predicts the typical behaviors of living tissues, but also easily accommodates isotropic, anisotropic and inhomogeneous materials, and local and large-range deformation. |
| first_indexed | 2025-11-14T06:18:33Z |
| format | Journal Article |
| id | curtin-20.500.11937-7936 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:18:33Z |
| publishDate | 2006 |
| publisher | UK Society for Modelling and Simulation |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-79362017-02-28T01:31:24Z Haptic deformation modelling through cellular neural network Zhong, Yongmin Shirinzadeh, B. Alici, G. Smith, J. soft objects haptic rendering and analogous systems CNN deformation This paper presents a new methodology for the deformation of soft objects by drawing an analogy between cellular neural network (CNN) and elastic deformation. The potential energy stored in an elastic body as a result of a deformation caused by an external force is propagated among mass points by nonlinear CNN activities. The novelty of the methodology is that soft object deformation is carried out from the perspective of energy propagation, and nonlinear material properties are modelled with nonlinear CNNs, rather than geometric nonlinearity as in most of the existing deformation methods. Integration with a haptic device has been achieved to simulate soft object deformation with force feedback. The proposed methodology not only predicts the typical behaviors of living tissues, but also easily accommodates isotropic, anisotropic and inhomogeneous materials, and local and large-range deformation. 2006 Journal Article http://hdl.handle.net/20.500.11937/7936 http://ijssst.info/Vol-07/No-8/cover.htm UK Society for Modelling and Simulation restricted |
| spellingShingle | soft objects haptic rendering and analogous systems CNN deformation Zhong, Yongmin Shirinzadeh, B. Alici, G. Smith, J. Haptic deformation modelling through cellular neural network |
| title | Haptic deformation modelling through cellular neural network |
| title_full | Haptic deformation modelling through cellular neural network |
| title_fullStr | Haptic deformation modelling through cellular neural network |
| title_full_unstemmed | Haptic deformation modelling through cellular neural network |
| title_short | Haptic deformation modelling through cellular neural network |
| title_sort | haptic deformation modelling through cellular neural network |
| topic | soft objects haptic rendering and analogous systems CNN deformation |
| url | http://ijssst.info/Vol-07/No-8/cover.htm http://hdl.handle.net/20.500.11937/7936 |