Measurement-based Hyper-elastic Material Identification and Real-time FEM Simulation for Haptic Rendering

In this paper, we propose a measurement-based modeling framework for hyper-elastic material identification and real-time haptic rendering. We build a custom data collection setup that captures shape deformation and response forces during compressive deformation of cylindrical material samples. We collected training and testing sets of data from four silicone objects having various material profiles. We design an objective function for material parameter identification by incorporating both shape deformation and reactive forces and utilize a genetic algorithm. We adopted an optimization-based Finite Element Method (FEM) for object deformation rendering. The numerical error of simulated forces was found to be perceptually negligible.

[1]  Gábor Székely,et al.  Identification of Spring Parameters for Deformable Object Simulation , 2007, IEEE Transactions on Visualization and Computer Graphics.

[2]  Nancy S. Pollard,et al.  Fast simulation of skeleton-driven deformable body characters , 2011, TOGS.

[3]  Hervé Delingette,et al.  Haptic rendering of hyperelastic models with friction , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[4]  Stephane Cotin,et al.  Asynchronous haptic simulation of contacting deformable objects with variable stiffness , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Sunghoon Yim,et al.  Data-Driven Haptic Modeling and Rendering of Viscoelastic and Frictional Responses of Deformable Objects , 2016, IEEE Transactions on Haptics.

[6]  Seokhee Jeon,et al.  Data-Driven Modeling and Rendering of Force Responses from Elastic Tool Deformation , 2018, Sensors.

[7]  Gábor Székely,et al.  Simultaneous Topology and Stiffness Identification for Mass-Spring Models Based on FEM Reference Deformations , 2004, MICCAI.

[8]  Marco Fratarcangeli,et al.  Vivace: a practical gauss-seidel method for stable soft body dynamics , 2016, ACM Trans. Graph..

[9]  Saeid Nahavandi,et al.  Openga, a C++ genetic algorithm library , 2017, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[10]  Rémy Willinger,et al.  Multiplicative Jacobian Energy Decomposition Method for Fast Porous Visco-Hyperelastic Soft Tissue Model , 2010, MICCAI.

[11]  Atsushi Konno,et al.  Haptic rendering of contact between rigid and deformable objects based on penalty method with implicit time integration , 2016, 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[12]  Thomas Knott,et al.  Accurate and adaptive contact modeling for multi-rate multi-point haptic rendering of static and deformable environments , 2016, Comput. Graph..

[13]  Dinesh K. Pai,et al.  Scanning physical interaction behavior of 3D objects , 2001, SIGGRAPH.

[14]  Jérémie Dequidt,et al.  Haptic rendering of interacting dynamic deformable objects simulated in real-time at different frequencies , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  Jie Li,et al.  ADMM ⊇ Projective Dynamics: Fast Simulation of Hyperelastic Models with Dynamic Constraints , 2017, IEEE Trans. Vis. Comput. Graph..

[16]  Jean-Claude Latombe,et al.  The forcegrid: a buffer structure for haptic interaction with virtual elastic objects , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[17]  Seokhee Jeon,et al.  Data-Driven Modeling of Anisotropic Haptic Textures: Data Segmentation and Interpolation , 2016, EuroHaptics.

[18]  Gábor Székely,et al.  Deformable haptic model generation through manual exploration , 2013, 2013 World Haptics Conference (WHC).

[19]  Bin Wang,et al.  Deformation capture and modeling of soft objects , 2015, ACM Trans. Graph..

[20]  Seokhee Jeon,et al.  Stimuli-Magnitude-Adaptive Sample Selection for Data-Driven Haptic Modeling , 2016, Entropy.

[21]  Thomas Knott,et al.  Accurate Contact Modeling for Multi-rate Single-point Haptic Rendering of Static and Deformable Environments , 2015, VRIPHYS.

[22]  Craig Schroeder,et al.  Optimization Integrator for Large Time Steps , 2014, IEEE Transactions on Visualization and Computer Graphics.

[23]  Gábor Székely,et al.  Data-Driven Haptic Rendering—From Viscous Fluids to Visco-Elastic Solids , 2009, IEEE Transactions on Haptics.

[24]  Stephane Cotin,et al.  Constraint-Based Haptic Rendering of Multirate Compliant Mechanisms , 2011, IEEE Transactions on Haptics.

[25]  Christian Duriez,et al.  Real-time simulation of contact and cutting of heterogeneous soft-tissues , 2014, Medical Image Anal..

[26]  M. Otaduy,et al.  Capture and modeling of non-linear heterogeneous soft tissue , 2009, ACM Trans. Graph..

[27]  Matthias Harders,et al.  Data-Driven Visuo-Haptic Rendering of Deformable Bodies , 2012, Immersive Multimodal Interactive Presence.

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

[29]  Matthias Harders,et al.  User-based evaluation of data-driven haptic rendering , 2010, TAP.

[30]  Wojciech Matusik,et al.  Data-driven finite elements for geometry and material design , 2015, ACM Trans. Graph..

[31]  Gábor Székely,et al.  Data-Driven Haptic Rendering of Visco-Elastic Effects , 2008, 2008 Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems.