Shi Yaoguang, Lü Xiaozhou, Wang Wenran, Zhou Xiaohui, Zhu Wensong
School of Aerospace Science and Technology, Xidian University, Xi'an 710071, China.
Micromachines (Basel). 2024 Dec 20;15(12):1513. doi: 10.3390/mi15121513.
Robotic devices with integrated tactile sensors can accurately perceive the contact force, pressure, sliding, and other tactile information, and they have been widely used in various fields, including human-robot interaction, dexterous manipulation, and object recognition. To address the challenges associated with the initial value drift, and to improve the durability and accuracy of the tactile detection for a robotic dexterous hand, in this study, a flexible tactile sensor is designed with high repeatability by introducing a supporting layer for pre-separation. The proposed tactile sensor has a detection range of 0-5 N with a resolution of 0.2 N, and the repeatability error is as relatively small as 1.5%. In addition, the response time of the proposed tactile sensor under loading and unloading conditions are 80 ms and 160 ms, respectively. Moreover, a three-dimensional force decoupling detection method is developed by distributing tactile sensor units on a non-coplanar robotic fingertip. Finally, using a backpropagation neural network, the classification and recognition processes of nine types of objects with different shapes and categories are realized, achieving an accuracy higher than 95%. The results show that the proposed three-dimensional force tactile sensing system could be beneficial for the delicate manipulation and recognition for robotic dexterous hands.
集成触觉传感器的机器人设备能够精确感知接触力、压力、滑动及其他触觉信息，已广泛应用于包括人机交互、灵巧操作和物体识别在内的各个领域。为应对与初始值漂移相关的挑战，并提高机器人灵巧手触觉检测的耐用性和准确性，本研究通过引入用于预分离的支撑层，设计了一种具有高重复性的柔性触觉传感器。所提出的触觉传感器检测范围为0至5 N，分辨率为0.2 N，重复性误差相对较小，仅为1.5%。此外，所提出的触觉传感器在加载和卸载条件下的响应时间分别为80 ms和160 ms。此外，通过在非共面机器人指尖上分布触觉传感器单元，开发了一种三维力解耦检测方法。最后，利用反向传播神经网络实现了对九种不同形状和类别的物体的分类和识别过程，准确率高于95%。结果表明，所提出的三维力触觉传感系统有利于机器人灵巧手的精细操作和识别。
