A cellular neural network methodology for deformable object simulation.
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.
Zhong
Yongmin
Y
Robotics and Mechatronics Research Laboratory, Department of Mechanical Engineering, Monash University, Clayton, VIC, Australia. Yongmin.Zhong@eng.monash.edu.au
Shirinzadeh
Bijan
B
Alici
Gursel
G
Smith
Julian
J
D032701:Biomimetics / Q000379:methods*; D003198:Computer Simulation; D003238:Connective Tissue / Q000502:physiology*; D004548:Elasticity; D008954:Models, Biological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D010812:Physical Stimulation / Q000379:methods; D014110:Touch / Q000502:physiology*