Multi-photon neuron embedded bionic skin for high-precision complex texture and object reconstruction perception research  

作　　者：Hongyu Zhou Chao Zhang Hengchang Nong Junjie Weng Dongying Wang Yang Yu Jianfa Zhang Chaofan Zhang Jinran Yu Zhaojian Zhang Huan Chen Zhenrong Zhang Junbo Yang 

机构地区：[1]College of Science,National University of Defense Technology,Changsha 410073,China [2]Key Laboratory of Multimedia Communication and Network Technology in Guangxi,School of Computer,Electronics and Information,Guangxi University,Nanning 530004,China [3]College of Intelligence Science and Technology,National University of Defense Technology,Changsha 410073,China [4]College of Meteorology and Oceanography,National University of Defense Technology,Changsha 410073,China [5]Hunan Provincial Key Laboratory of Novel Nano-Optoelectronic,Information Materials and Devices,National University of Defense Technology,Changsha 410073,China [6]College of Advanced Interdisciplinary Studies,National University of Defense Technology,Changsha 410073,China

出　　处：《Opto-Electronic Advances》2025年第2期29-44,共16页光电进展(英文)

基　　金：supported by National Natural Sciences Foundation of China grant No.62275269.

摘　　要：Attributable to the complex distribution of tactile vesicles under the skin and the ability of the brain to process specific tactile parameters(shape,hardness,and surface texture),human skin can have the capacity for tactile spatial reconstruction and visualization of complex object geometry and surface texture.However,current haptic sensor technologies are predominantly point sensors,which do not have an interlaced distribution structure similar to that of haptic vesicles,limiting their potential in human-computer interaction applications.Here,we report an optical microfiber array skin(OMAS)imitating tactile vesicle interlaced structures for tactile visualization and object reconstruction sensing.This device is characterized by high sensitivity(−0.83 N/V)and fast response time(38 ms).We demonstrate that combining the signals collected by the OMAS with appropriate artificial intelligence algorithms enables the recognition of objects with different hardnesses and shapes with 100%accuracy.It also allows for the classification of fabrics with different surface textures with 98.5%accuracy and Braille patterns with 99%accuracy.As a proof-of-concept,we integrated OMAS into a robot arm to select mahjong among six common objects and successfully recognize its suits by touch,which provides a new solution for tactile sensory processing for human-computer interaction.

关 键 词：multiphoton neurons human-computer interaction tactile sensing tactile imaging tactile spatial reconstruction 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]