A tactile perception method with flexible grating structural color

作     者：Yuze Qiu Chunfei Yan Yan Zhang Shengxuan Yang Xiang Yao Fawen Ai Jinjin Zheng Shiwu Zhang Xinge Yu Erbao Dong Yuze Qiu;Chunfei Yan;Yan Zhang;Shengxuan Yang;Xiang Yao;Fawen Ai;Jinjin Zheng;Shiwu Zhang;Xinge Yu;Erbao Dong

作者机构：Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China Institute of Humanoid Robots Department of Precision Machinery and Precision Instrumentation University of Science and Technology of China Department of Biomedical Engineering City University of Hong Kong 

出 版 物：《National Science Review》 (国家科学评论(英文版))

年 卷 期：2025年第12卷第1期

页      面：229-242页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 080202[工学-机械电子工程] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China (61773358) Research Grants Council of the Hong Kong Special Administrative Region (RFS2324-1S03) 

主　　题：force and tactile sensing structural color vision-based tactile sensor deep learning wave optics 

摘      要：Affordable high-resolution cameras and state-of-the-art computer vision techniques have led to the emergence of various vision-based tactile sensors. However, current vision-based tactile sensors mainly depend on geometric optics or marker tracking for tactile assessments, resulting in limited performance. To solve this dilemma, we introduce optical interference patterns as the visual representation of tactile information for flexible tactile sensors. We propose a novel tactile perception method and its corresponding sensor, combining structural colors from flexible blazed gratings with deep learning. The richer structural colors and finer data processing foster the tactile estimation performance. The proposed sensor has an overall normal force magnitude accuracy of 6 m N, a planar resolution of 79 μm and a contact-depth resolution of 25 μm. This work presents a promising tactile method that combines wave optics, soft materials and machine learning. It performs well in tactile measurement, and can be expanded into multiple sensing fields.