Abstract
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.
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References
Andrews, S., Lang, J.: Haptic texturing based on real-world samples. In: IEEE International Workshop on Haptic, Audio and Visual Environments and Games (HAVE 2007), pp. 142–147. IEEE (2007)
Basdogan, C., Ho, C., Srinivasan, M.A.: A ray based haptic rendering technique for displaying shape and texture of 3D objects in virtual environments. In: ASME Winter Annual Meeting, vol. 61, pp. 77–84 (1997)
Culbertson, H., Romano, J., Castillo, P., Mintz, M., Kuchenbecker, K.: Refined methods for creating realistic haptic virtual textures from tool-mediated contact acceleration data. In: 2012 IEEE Haptics Symposium (HAPTICS), pp. 385–391, March 2012
Culbertson, H., Unwin, J., Kuchenbecker, K.: Modeling and rendering realistic textures from unconstrained tool-surface interactions. IEEE Trans. Haptics 7(3), 381–393 (2014)
Erkelens, J.S.: Autoregressive modeling for speech coding: estimation, interpolation and quantization, chapter 4: spectral interpolation. TU Delft, Delft University of Technology (1996)
Fritz, J.P., Barner, K.E.: Stochastic models for haptic texture. In: Photonics East 1996, pp. 34–44. International Society for Optics and Photonics (1996)
Guruswamy, V.L., Lang, J., Lee, W.S.: Modelling of haptic vibration textures with infinite-impulse-response filters. In: IEEE International Workshop on Haptic Audio Visual Environments and Games (HAVE 2009), pp. 105–110. IEEE (2009)
Hayes, M.H.: Statistical Digital Signal Processing and Modeling. Wiley, New York (2009)
Iske, A.: Multiresolution Methods in Scattered Data Modelling. Lecture Notes in Computational Science and Engineering, vol. 37. Springer, Heidelberg (2004)
Katz, D.: The World of Touch (le krueger, trans.). Erlbaum, Mahwah (1989). (Original work published 1925)
Keogh, E., Chu, S., Hart, D., Pazzani, M.: Segmenting time series: a survey and novel approach. In: Data Mining in Time Series Databases, vol. 57, pp. 1–22 (2004)
Kim, L., Kyrikou, A., Sukhatme, G.S., Desbrun, M.: An implicit-based haptic rendering technique. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3, pp. 2943–2948. IEEE (2002)
Okamura, A.M., Dennerlein, J.T., Howe, R.D.: Vibration feedback models for virtual environments. In: 1998 IEEE International Conference on Robotics and Automation, Proceedings, vol. 1, pp. 674–679. IEEE (1998)
Okamura, A.M., Kuchenbecker, K.J., Mahvash, M.: Measurement-based modeling for haptic rendering. In: Haptic Rendering: Foundations, Algorithms, and Applications, pp. 443–467 (2008)
Romano, J.M., Kuchenbecker, K.J.: Creating realistic virtual textures from contact acceleration data. IEEE Trans. Haptics 5(2), 109–119 (2012)
Romano, J.M., Yoshioka, T., Kuchenbecker, K.J.: Automatic filter design for synthesis of haptic textures from recorded acceleration data. In: 2010 IEEE International Conference on Robotics and Automation (ICRA), pp. 1815–1821. IEEE (2010)
Shin, S., Osgouei, R., Kim, K.D., Choi, S.: Data-driven modeling of isotropic haptic textures using frequency-decomposed neural networks. In: 2015 IEEE World Haptics Conference (WHC), pp. 131–138, June 2015
Vasudevan, H., Manivannan, M.: Recordable haptic textures. In: IEEE International Workshop on Haptic Audio Visual Environments and Their Applications (HAVE 2006), pp. 130–133. IEEE (2006)
Wall, S.A., Harwin, W.S.: Modelling of surface identifying characteristics using fourier series. In: Proceedings of the ASME Dynamic Systems and Control Division (DSC), Symposium on Haptic Interfaces for Virtual Environments and Teleoperators, vol. 67, pp. 65–71 (1999)
Wright, S.J., Nowak, R.D., Figueiredo, M.A.: Sparse reconstruction by separable approximation. IEEE Trans. Signal Process. 57(7), 2479–2493 (2009)
Zhang, Z., Xu, Y., Yang, J., Li, X., Zhang, D.: A survey of sparse representation: algorithms and applications. IEEE Access 3, 490–530 (2015)
Acknowledgments
This research was supported by Basic Science Research Program through the NRF of Korea (NRF-2014R1A1A2057100), by Global Frontier Program through NTF of Korea (NRF-2012M3A6A3056074), and by ERC program through NRF of Korea (2011-0030075).
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Abdulali, A., Jeon, S. (2016). Data-Driven Modeling of Anisotropic Haptic Textures: Data Segmentation and Interpolation. In: Bello, F., Kajimoto, H., Visell, Y. (eds) Haptics: Perception, Devices, Control, and Applications. EuroHaptics 2016. Lecture Notes in Computer Science(), vol 9775. Springer, Cham. https://doi.org/10.1007/978-3-319-42324-1_23
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DOI: https://doi.org/10.1007/978-3-319-42324-1_23
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