Modern Visual-Based Tactile Sensors (VBTSs) use cost-effective cameras to track elastomer deformation, but struggle with ambient light interference. Solutions typically involve using internal LEDs and blocking external light, thus adding complexity. Creating a VBTS resistant to ambient light with just a camera and an elastomer remains a challenge. In this work, we introduce WStac, a self-illuminating VBTS comprising a mechanoluminescence (ML) whisker elastomer, camera, and 3D printed parts. The ML whisker elastomer, inspired by the touch sensitivity of vibrissae, offers both light isolation and high ML intensity under stress, thereby removing the necessity for additional LED modules. With the incorporation of machine learning, the sensor effectively utilizes the dynamic contact variations of 25 whiskers to successfully perform tasks like speed regression, directional identification, and texture classification. Videos are available at: https://sites.google.com/view/wstac/.

翻译：现代视觉触觉传感器（VBTS）采用低成本摄像头追踪弹性体形变，但易受环境光干扰。现有解决方案通常依赖内置LED并隔绝外部光线，因而增加了系统复杂性。如何仅用摄像头与弹性体构建抗环境光干扰的VBTS仍是一项挑战。本研究提出WStac——一种自照明型VBTS，由力致发光（ML）触须弹性体、摄像头及3D打印部件组成。受振动触觉敏感性启发，ML触须弹性体兼具光隔离与应力下高ML强度特性，从而无需额外LED模块。通过引入机器学习，传感器利用25根触须的动态接触变化，成功实现速度回归、方向识别及纹理分类等任务。演示视频参见：https://sites.google.com/view/wstac/。