In this work, we use MEMS microphones as vibration sensors to simultaneously classify texture and estimate contact position and velocity. Vibration sensors are an important facet of both human and robotic tactile sensing, providing fast detection of contact and onset of slip. Microphones are an attractive option for implementing vibration sensing as they offer a fast response and can be sampled quickly, are affordable, and occupy a very small footprint. Our prototype sensor uses only a sparse array (8-9 mm spacing) of distributed MEMS microphones (<$1, 3.76 x 2.95 x 1.10 mm) embedded under an elastomer. We use transformer-based architectures for data analysis, taking advantage of the microphones' high sampling rate to run our models on time-series data as opposed to individual snapshots. This approach allows us to obtain 77.3% average accuracy on 4-class texture classification (84.2% when excluding the slowest drag velocity), 1.8 mm mean error on contact localization, and 5.6 mm/s mean error on contact velocity. We show that the learned texture and localization models are robust to varying velocity and generalize to unseen velocities. We also report that our sensor provides fast contact detection, an important advantage of fast transducers. This investigation illustrates the capabilities one can achieve with a MEMS microphone array alone, leaving valuable sensor real estate available for integration with complementary tactile sensing modalities.

翻译：在本文中，我们利用MEMS麦克风作为振动传感器，同步实现纹理分类、接触位置与速度的估计。振动传感器是人类和机器人触觉感知的重要组成部分，能够快速检测接触和滑移的起始。麦克风是实现振动传感的优选方案，因其响应迅速、可实现高速采样、成本低廉且占用极小空间。我们的原型传感器仅采用稀疏分布（间距8-9毫米）的嵌入式MEMS麦克风阵列（单价低于1美元，尺寸3.76×2.95×1.10毫米），并置于弹性体下方。我们采用基于Transformer的架构进行数据分析，充分利用麦克风的高采样率，以时间序列数据而非单帧快照作为模型输入。该方法在四类纹理分类任务中达到77.3%的平均准确率（排除最慢拖拽速度时可达84.2%），接触定位平均误差为1.8毫米，接触速度平均误差为5.6毫米/秒。研究表明，学习得到的纹理与定位模型对速度变化具有鲁棒性，并能泛化至未见速度条件。此外，我们的传感器实现了快速接触检测，凸显了高速换能器的重要优势。本项研究揭示了仅凭MEMS麦克风阵列即可实现的能力，为与互补型触觉传感模态的集成预留了宝贵的传感器空间资源。