Xu Qian, Yang Zhiwei, Wang Zhengjun, Wang Ruoqin, Zhang Boyang, Cheung YikKin, Jiao Rui, Shi Fan, Hong Wei, Yu Hongyu
Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, 999077, China.
Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, Guangdong Province, 518055, China.
Adv Sci (Weinh). 2025 May;12(18):e2414580. doi: 10.1002/advs.202414580. Epub 2025 Mar 19.
With substantial advances in materials science and electronics, flexible tactile sensors have emerged as a promising sector with extensive applications, notably in human-machine interactions. However, achieving large-area sensing with few sensing units at a low cost remains a challenge; the use of sensor arrays will complicate wiring and increase costs. To solve these issues, a sandwich Miura-ori (SMo)-enabled super-resolution tactile skin capable of resolving normal and shear forces is proposed, and a theoretical model that incorporates the impact of actual manufacturing process is also developed, enabling the model to be employed for different tactile skins following calibration. Using machine learning techniques, the proposed tactile skin can accurately localize touch inputs (average localization error of 1.89 mm) and estimate the external force (average estimation error of 8%). Furthermore, a curved SMo skin is designed and fabricated using the tessellation algorithm, then installed on a robotic arm to control the motion, demonstrating its potential in human-machine interactions. This research introduces a straightforward and cost-effective approach to the design and manufacturing of super-resolution tactile skins, and it also offers a valuable solution for future large-area tactile sensor technologies.
随着材料科学和电子学的重大进展，柔性触觉传感器已成为一个具有广泛应用前景的领域，特别是在人机交互方面。然而，以低成本用少量传感单元实现大面积传感仍然是一个挑战；使用传感器阵列会使布线复杂化并增加成本。为了解决这些问题，提出了一种能够分辨法向力和剪切力的三明治型三浦折纸（SMo）超分辨率触觉皮肤，并开发了一个纳入实际制造过程影响的理论模型，使得该模型在校准后可用于不同的触觉皮肤。利用机器学习技术，所提出的触觉皮肤能够准确地定位触摸输入（平均定位误差为1.89毫米）并估计外力（平均估计误差为8%）。此外，使用镶嵌算法设计并制造了一种弯曲的SMo皮肤，然后将其安装在机器人手臂上以控制运动，展示了其在人机交互中的潜力。本研究为超分辨率触觉皮肤的设计和制造引入了一种简单且经济高效的方法，也为未来大面积触觉传感器技术提供了一个有价值的解决方案。
