Data-driven modeling of isotropic haptic textures using frequency-decomposed neural networks
Data-driven modeling of isotropic haptic textures using frequency-decomposed neural networks
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DOI：
10.1109/whc.2015.7177703
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发表时间：
2015-06
期刊：
影响因子：
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通讯作者：
Sunghwan Shin;Reza Haghighi Osgouei;Ki-duk Kim;Seungmoon Choi
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文献类型：
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作者：
Sunghwan Shin;Reza Haghighi Osgouei;Ki-duk Kim;Seungmoon Choi
本文提出了一种数据驱动的各向同性触觉纹理建模的新方法，该方法使用频率分解神经网络，从触控笔在不同扫描速度和法向力的纹理表面上扫描时捕获的接触加速度数据中获取数据。我们首先描述了一种机动纹理扫描仪，它是为了在各种条件下准确和容易地收集数据而开发的。然后，我们提出了两个具有不同拓扑结构的神经网络模型：一个统一模型将所有加速度数据、扫描速度和法向力作为输入变量馈送到单个大型神经网络；一个分解模型由许多较小的神经网络组成，每个神经网络都使用一对扫描速度和法向力的加速度数据进行训练。实际样本实验表明，统一模型在光谱均方根误差方面具有较好的交叉验证能力，其性能可与文献中最好的模型相媲美。本文还介绍了通过扩展统一模型实现各向异性纹理建模的一些初步结果。
This paper presents a new approach to data-driven modeling of isotropic haptic textures using frequency-decomposed neural networks from the contact acceleration data that are captured when a stylus is scanned on a textured surface with diverse scanning velocities and normal forces. We first describe a motorized texture scanner that was developed for accurate and easy data collection under a wide variety of conditions. We then propose two neural network models with different topologies: a unified model that feeds all of acceleration data, scanning velocity, and normal force as input variables to a single large neural network and a decomposed model that consists of a number of smaller neural networks each of which is trained with the acceleration data for a pair of scanning velocity and normal force. An experiment with real samples showed that the unified model has better cross-validation ability in terms of spectral rms errors and its performance is comparable to the best available in the literature. We also present some preliminary results of anisotropic texture modeling achieved by extending the unified model.