Department of Mechanical Engineering, National Cheng-Kung University, Tainan 70101, Taiwan.
Center for Micro/Nano Science and Technology, National Cheng Kung University, Tainan 70101, Taiwan.
Sensors (Basel). 2020 May 4;20(9):2619. doi: 10.3390/s20092619.
A tactile position sensing system based on the sensing of acoustic waves and analyzing with artificial intelligence is proposed. The system comprises a thin steel plate with multiple piezoelectric transducers attached to the underside, to excite and detect Lamb waves (or plate waves). A data acquisition and control system synchronizes the wave excitation and detection and records the transducer signals. When the steel plate is touched by a finger, the waveform signals are perturbed by wave absorption and diffraction effects, and the corresponding changes in the output signal waveforms are sent to a convolutional neural network (CNN) model to predict the x- and y-coordinates of the finger contact position on the sensing surface. The CNN model is trained by using the experimental waveform data collected using an artificial finger carried by a three-axis motorized stage. The trained model is then used in a series of tactile sensing experiments performed using a human finger. The experimental results show that the proposed touch sensing system has an accuracy of more than 95%, a spatial resolution of 1 × 1 cm, and a response time of 60 ms.
提出了一种基于声波感应和人工智能分析的触觉位置感应系统。该系统包括一个附有多个压电换能器的薄钢板，用于激励和检测兰姆波（或板波）。一个数据采集和控制系统同步进行波的激励和检测，并记录换能器信号。当钢板被手指触摸时，波形信号会受到波吸收和衍射效应的干扰，相应的输出信号波形变化会被发送到卷积神经网络（CNN）模型，以预测手指在感应表面上的 x 和 y 坐标接触位置。CNN 模型通过使用带有三轴电动台的人工手指收集的实验波形数据进行训练。然后，在使用人手指进行的一系列触觉感应实验中使用训练好的模型。实验结果表明，所提出的触摸感应系统具有超过 95%的准确率、1×1cm 的空间分辨率和 60ms 的响应时间。
