A cellular neural network methodology for deformable object simulation.
IEEE Trans Inf Technol Biomed
; 10(4): 749-62, 2006 Oct.
Article
em En
| MEDLINE
| ID: mdl-17044409
ABSTRACT
This paper presents a new methodology to simulate soft object deformation by drawing an analogy between a 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 a nonlinear CNN. The novelty of the methodology is that 1) CNN techniques are established to describe the potential energy distribution of the deformation for extrapolating internal forces and 2) nonlinear materials are modeled with nonlinear CNNs rather than geometric nonlinearity. Integration with a haptic device has been achieved for deformable object simulation with force feedback. The proposed methodology not only predicts the typical behaviors of living tissues, but it also accommodates isotropic, anisotropic, and inhomogeneous materials, as well as local and large-range deformation.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Tato
/
Redes Neurais de Computação
/
Tecido Conjuntivo
/
Biomimética
/
Modelos Biológicos
/
Rede Nervosa
Tipo de estudo:
Evaluation_studies
/
Prognostic_studies
Idioma:
En
Revista:
IEEE Trans Inf Technol Biomed
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2006
Tipo de documento:
Article
País de afiliação:
Austrália