摘要
本文设计了一种面向纹理识别的便携式触觉传感器,该传感器利用光纤光栅(FBG)识别检测不同的纹理和滑动接触速度,且便于机器人系统集成,同时对硬件和软件配置要求低,受环境影响小。在三维建模基础上对传感器结构进行静力学分析并优化,提高FBG对力觉信息的灵敏度;专门设计并搭建了实验平台,对传感器进行静力标定实验和复杂多纹理表面检测实验。通过实验数据的时频分析,验证了该传感器可以识别不同的滑动接触速度和不同的纹理。在该传感器中,FBG3的灵敏度最高,加载时,平均灵敏度约为51.1 pm/N,线性度为0.998;卸载时,平均灵敏度约为50.8 pm/N,线性度为0.998。FBG2的重复性误差和迟滞性误差最大,分别为2.35%和2.23%。
In this article,a portable tactile sensor for texture recognition is designed.The sensor uses fiber Bragg grating(FBG)to recognize and detect different textures and sliding contact speed,which is convenient for robot system integration.Meanwhile,it has low requirement on hardware and software configuration,which is less affected by the environment.Based on the three-dimensional modeling,the sensor structure is statically analyzed and optimized to improve the sensitivity of FBG to force sensing information.A special experimental platform is designed and established to carry out static calibration experiments and complex multi-texture surface detection experiments on the sensor.Through time-frequency analysis of experimental data,it shows that the sensor can recognize different sliding contact speed and different texture.Among the sensors,FBG3 has the highest sensitivity,with an average sensitivity of about 51.1 pm/N and a linearity of 0.998 under loading.When it is unloaded,the average sensitivity is about 50.8 pm/N,and the linearity is 0.998.The repeatability error and hysteresis error of FBG2 are the largest,which are 2.35%and 2.23%,respectively.
作者
张剑敏
熊鹏文
韦琦
刘国平
刘继忠
Zhang Jianmin;Xiong Pengwen;Wei Qi;Liu Guoping;Liu Jizhong(School of Information Engineering,Nanchang University,Nanchang 330031,China;School of Advanced Manufacturing,Nanchang University,Nanchang 330031,China;Department of Automation,University of Science and Technology of China,Hefei 230026,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2022年第10期66-73,共8页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(61903175,62163024)
江西省主要学科学术和技术带头人项目(20204BCJ23006)
江西省学位与研究生教育教学改革研究项目(JXYJG-2019-019)资助。
关键词
光纤布拉格光栅
假手
触觉传感器
纹理识别
fiber Bragg grating
prosthetic hand
tactile sensor
texture recognition