Data-Driven Modeling of Anisotropic Haptic Textures: Data Segmentation and Interpolation

This paper presents a new data-driven approach for modeling haptic responses of textured surfaces with homogeneous anisotropic grain. The approach assumes unconstrained tool-surface interaction with a rigid tool for collecting data during modeling. The directionality of the texture is incorporated in modeling by including 2 dimensional velocity vector of user’s movement as an input for the data interpolation model. In order to handle increased dimensionality of the input, improved input-data-space-based segmentation algorithm is introduced, which ensures evenly distributed and correctly segmented samples for interpolation model building. In addition, new Radial Basis Function Network is employed as interpolation model, allowing more general and flexible data-driven modeling framework. The estimation accuracy of the approach is evaluated through cross-validation in spectral domain using 8 real surfaces with anisotropic texture.

[1]  S. Wall,et al.  Modelling of Surface Identifying Characteristics Using Fourier Series , 1999, Dynamic Systems and Control.

[2]  Seungmoon Choi,et al.  Data-driven modeling of isotropic haptic textures using frequency-decomposed neural networks , 2015, 2015 IEEE World Haptics Conference (WHC).

[3]  Eamonn J. Keogh,et al.  Segmenting Time Series: A Survey and Novel Approach , 2002 .

[4]  David Zhang,et al.  A Survey of Sparse Representation: Algorithms and Applications , 2015, IEEE Access.

[5]  S. Andrews,et al.  Haptic Texturing based on Real-World Samples , 2007, 2007 IEEE International Workshop on Haptic, Audio and Visual Environments and Games.

[6]  Allison M. Okamura,et al.  Measurement-Based Modeling for Haptic Rendering , 2008 .

[7]  Takashi Yoshioka,et al.  Automatic filter design for synthesis of haptic textures from recorded acceleration data , 2010, 2010 IEEE International Conference on Robotics and Automation.

[8]  Max Mintz,et al.  Refined methods for creating realistic haptic virtual textures from tool-mediated contact acceleration data , 2012, 2012 IEEE Haptics Symposium (HAPTICS).

[9]  Stephen J. Wright,et al.  Sparse Reconstruction by Separable Approximation , 2008, IEEE Transactions on Signal Processing.

[10]  Won-Sook Lee,et al.  Modelling of haptic vibration textures with infinite-impulse-response filters , 2009, 2009 IEEE International Workshop on Haptic Audio visual Environments and Games.

[11]  Heather Culbertson,et al.  Modeling and Rendering Realistic Textures from Unconstrained Tool-Surface Interactions , 2014, IEEE Transactions on Haptics.

[12]  M. Manivannan,et al.  Recordable Haptic textures , 2006, 2006 IEEE International Workshop on Haptic Audio Visual Environments and their Applications (HAVE 2006).

[13]  Joseph M. Romano,et al.  Creating Realistic Virtual Textures from Contact Acceleration Data , 2012, IEEE Transactions on Haptics.

[14]  Cagatay Basdogan,et al.  A Ray-Based Haptic Rendering Technique for Displaying Shape and Texture of 3D Objects in Virtual Environments , 1997, Dynamic Systems and Control.

[15]  Allison M. Okamura,et al.  Vibration feedback models for virtual environments , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[16]  Gaurav S. Sukhatme,et al.  An implicit-based haptic rendering technique , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[17]  Armin Iske,et al.  Multiresolution Methods in Scattered Data Modelling , 2004, Lecture Notes in Computational Science and Engineering.

[18]  Monson H. Hayes,et al.  Statistical Digital Signal Processing and Modeling , 1996 .

[19]  Kenneth E. Barner,et al.  Stochastic models for haptic texture , 1996, Other Conferences.

[20]  J. S. Erkelens,et al.  Autoregressive modeling for speech coding: Estimation, interpolation and quantization , 1996 .