Chatterjee Sitangshu, Tan Sylvia, Choi Changhyun, Kuchibhotla Aditya, Wan Guangchao, Peshkin Michael A, Colgate J Edward, Hipwell M Cynthia
Department of Mechanical Engineering, Texas A&M University, 3123 TAMU, College Station, TX 77843, USA.
Department of Mechanical Engineering, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA.
PNAS Nexus. 2024 Oct 16;3(10):pgae468. doi: 10.1093/pnasnexus/pgae468. eCollection 2024 Oct.
The ability to render realistic texture perception using haptic devices has been consistently challenging. A key component of texture perception is roughness. When we touch surfaces, mechanoreceptors present under the skin are activated and the information is processed by the nervous system, enabling perception of roughness/smoothness. Several distributed haptic devices capable of producing localized skin stretch have been developed with the aim of rendering realistic roughness perception; however, current state-of-the-art devices rely on device fabrication and psychophysical experimentation to determine whether a device configuration will perform as desired. Predictive models can elucidate physical mechanisms, providing insight and a more effective design iteration process. Since existing models (1, 2) are derived from responses to normal stimuli only, they cannot predict the performance of laterally actuated devices which rely on frictional shear forces to produce localized skin stretch. They are also unable to predict the augmentation of roughness perception when the actuators are spatially dispersed across the contact patch or actuated with a relative phase difference (3). In this study, we have developed a model that can predict the perceived roughness for arbitrary external stimuli and validated it against psychophysical experimental results from different haptic devices reported in the literature. The model elucidates two key mechanisms: (i) the variation in the change of strain across the contact patch can predict roughness perception with strong correlation and (ii) the inclusion of lateral shear forces is essential to correctly predict roughness perception. Using the model can accelerate device optimization by obviating the reliance on trial-and-error approaches.
使用触觉设备实现逼真的纹理感知能力一直具有挑战性。纹理感知的一个关键组成部分是粗糙度。当我们触摸表面时，皮肤下的机械感受器会被激活，信息由神经系统处理，从而使我们能够感知粗糙度/光滑度。为了实现逼真的粗糙度感知，已经开发了几种能够产生局部皮肤拉伸的分布式触觉设备；然而，当前的先进设备依赖于设备制造和心理物理学实验来确定设备配置是否能按预期运行。预测模型可以阐明物理机制，提供见解并实现更有效的设计迭代过程。由于现有模型（1, 2）仅从对正常刺激的响应中推导得出，它们无法预测依赖摩擦剪切力产生局部皮肤拉伸的横向驱动设备的性能。它们也无法预测当致动器在接触区域上空间分散或具有相对相位差驱动时粗糙度感知的增强情况（3）。在本研究中，我们开发了一个模型，该模型可以预测任意外部刺激下的感知粗糙度，并根据文献中报道的不同触觉设备的心理物理学实验结果对其进行了验证。该模型阐明了两个关键机制：（i）接触区域上应变变化的差异可以高度相关地预测粗糙度感知，并且（ii）纳入横向剪切力对于正确预测粗糙度感知至关重要。使用该模型可以避免依赖试错法，从而加速设备优化。
