Seongwon Cho
Seungmoon Choi
ABSTRACT
We propose a data-driven method for realistic texture rendering on an electrovibration display. To compensate the nonlinear dynamics of an electrovibration display, we use nonlinear autoregressive with external input (NARX) neural networks as an inverse dynamics model of an electrovibration display. The neural networks are trained with lateral forces resulting from actuating the display with a pseudo-random binary signal (PRBS). The lateral forces collected from the textured surface with various scanning velocities and normal forces are fed into the neural network to generate the actuation signal for the display. For arbitrary scanning velocity and normal force, we apply the two-step interpolation scheme between the closest neighbors in the velocity-force grid.
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- Jeonggoo Kang, Heewon Kim, Seungmoon Choi, Ki-Duk Kim, and Jeha Ryu. 2017. Investigation on Low Voltage Operation of Electrovibration Display. IEEE Transactions on Haptics 10, 3 (jul 2017), 371--381. http://dx.doi.org/10.1109/toh.2016.2635145Google ScholarDigital Library
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- Reza Haghighi Osgouei, Jin Ryong Kim, and Seungmoon Choi. 2016. Identification of primitive geometrical shapes rendered using electrostatic friction display. In 2016 IEEE Haptics Symposium (HAPTICS). IEEE. http://dx.doi.org/10.1109/haptics.2016.7463177Google ScholarCross Ref
- Reza Haghighi Osgouei, Sunghwan Shin, Jin Ryong Kim, and Seungmoon Choi. 2018. An Inverse Neural Network Model for Data-driven Texture Rendering on Electrovibration Display. In Proceedings of the IEEE Haptics Symposium. 270--277.Google ScholarCross Ref
- Sunghoon Yim, Seokhee Jeon, and Seungmoon Choi. 2016. Data-Driven Haptic Modeling and Rendering of Viscoelastic and Frictional Responses of Deformable Objects. IEEE Trans. Haptics 9, 4 (2016), 548--559.Google ScholarDigital Library
Index Terms
- Data-driven Texture Modeling and Rendering on Electrovibration Display
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