An autowave based methodology for deformable object simulation

Abstract This paper presents a new methodology for deformable object simulation by drawing an analogy between autowaves 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 non-linear autowaves. The novelty of the methodology is that autowave techniques are established to describe the potential energy distribution of a deformation for extrapolating internal elastic forces, and non-linear material properties are modelled with non-linear autowaves other than geometric non-linearity. A haptic virtual reality system has been developed for deformation simulation with force feedback. The proposed methodology not only deals with large-range deformations, but also accommodates isotropic, anisotropic and inhomogeneous materials by simply modifying diffusion coefficients.

[1]  L. Chua,et al.  Simulating nonlinear waves and partial differential equations via CNN. I. Basic techniques , 1995 .

[2]  Vadim N. Biktashev,et al.  Autowave principles for parallel image processing , 1991 .

[3]  W Müller,et al.  Real-time simulation of tissue deformation for the nasal endoscopy simulator (NES). , 1999, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[4]  J. W. Humberston.  Classical mechanics , 1980, Nature.

[5]  John C. Platt,et al.  Elastically deformable models , 1987, SIGGRAPH.

[6]  Morozov,et al.  Autowaves in moving excitable media , 2004 .

[7]  Kup-Sze Choi,et al.  Interactive deformation of soft tissues with haptic feedback for medical learning , 2003, IEEE Transactions on Information Technology in Biomedicine.

[8]  Cagatay Basdogan,et al.  Haptics in minimally invasive surgical simulation and training , 2004, IEEE Computer Graphics and Applications.

[9]  Andrew P. Witkin,et al.  Large steps in cloth simulation , 1998, SIGGRAPH.

[10]  Dinesh K. Pai,et al.  ArtDefo: accurate real time deformable objects , 1999, SIGGRAPH.

[11]  R. Kenedi,et al.  REVIEW ARTICLE: Tissue mechanics , 1975 .

[12]  V. I. Krinsky,et al.  Autowaves: Results, Problems, Outlooks , 1984 .

[13]  Yu Zhang,et al.  A new physical model with multilayer architecture for facial expression animation using dynamic adaptive mesh , 2004, IEEE Transactions on Visualization and Computer Graphics.

[14]  Y. Fung,et al.  Biomechanics: Mechanical Properties of Living Tissues , 1981 .

[15]  Ole Jakob Elle,et al.  Realism in surgical simulators with free-form geometric modeling , 2001, CARS.

[16]  Mariano Alcañiz Raya,et al.  Real-time deformable models for surgery simulation: a survey , 2005, Comput. Methods Programs Biomed..

[17]  Mathieu Desbrun,et al.  Dynamic real-time deformations using space & time adaptive sampling , 2001, SIGGRAPH.

[18]  Lin-Bao Yang,et al.  Cellular neural networks: theory , 1988 .

[19]  Leon O. Chua,et al.  The CNN paradigm , 1993 .

[20]  Luigi Fortuna,et al.  CNN wave based computation for robot navigation planning , 2004, 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512).

[21]  Vicente Pérez-Muñuzuri,et al.  Autowaves for Image Processing on a Two-Dimensional CNN Array of Excitable Nonlinear Circuits: Flat and Wrinkled Labyrinths V. Perez-Mufiuzuri, V. Perez-Villar, and Leon 0. Chua, Fellow, ZEEE , 1993 .

[22]  Hervé Delingette,et al.  Improving realism of a surgery simulator: linear anisotropic elasticity, complex interactions and force extrapolation , 2002, Comput. Animat. Virtual Worlds.

[23]  Stephane Cotin,et al.  A hybrid elastic model for real-time cutting, deformations, and force feedback for surgery training and simulation , 2000, The Visual Computer.

[24]  M. Bro-Nielsen,et al.  Finite element modeling in surgery simulation , 1998, Proc. IEEE.

[25]  Taku Komura,et al.  Topology matching for fully automatic similarity estimation of 3D shapes , 2001, SIGGRAPH.

[26]  Leon O. Chua,et al.  Autonomous cellular neural networks: a unified paradigm for pattern formation and active wave propagation , 1995 .

[27]  Herve Delingette,et al.  Real-Time Elastic Deformations of Soft Tissues for Surgery Simulation , 1999, IEEE Trans. Vis. Comput. Graph..

[28]  Mariano Alcañiz Raya,et al.  A new approach for the real-time simulation of tissue deformations in surgery simulation , 2001, Comput. Methods Programs Biomed..

[29]  V. Krinsky.  Self-Organization Autowaves and Structures Far from Equilibrium , 1984 .

[30]  Hervé Delingette,et al.  Non-linear anisotropic elasticity for real-time surgery simulation , 2003, Graph. Model..

[31]  P. Arena,et al.  Reaction-diffusion CNN algorithms to generate and control artificial locomotion , 1999 .

[32]  L. Chua,et al.  Simulating nonlinear waves and partial differential equations via CNN. II. Typical examples , 1995 .

[33]  Weeratunge Malalasekera,et al.  An introduction to computational fluid dynamics - the finite volume method , 2007 .

[34]  Vernadsky Prospect.  Autowaves in the moving excitable media , 2004 .