遥操作
可穿戴计算机
接口(物质)
计算机硬件
信号(编程语言)
计算机科学
嵌入式系统
电阻器
人工智能
机器人
工程类
模拟
计算机视觉
电气工程
最大气泡压力法
气泡
电压
并行计算
程序设计语言
作者
Chuanyu Zhong,Shumi Zhao,Yang Liu,Zhijun Li,Zhen Kan,Yingqing Feng
出处
期刊:Robotica
[Cambridge University Press]
日期:2022-09-16
卷期号:41 (3): 1025-1038
被引量:4
标识
DOI:10.1017/s026357472200131x
摘要
Abstract Electronic skin (e-skin) is playing an increasingly important role in health detection, robotic teleoperation, and human-machine interaction, but most e-skins currently lack the integration of on-site signal acquisition and transmission modules. In this paper, we develop a novel flexible wearable e-skin sensing system with 11 sensing channels for robotic teleoperation. The designed sensing system is mainly composed of three components: e-skin sensor, customized flexible printed circuit (FPC), and human-machine interface. The e-skin sensor has 10 stretchable resistors distributed at the proximal and metacarpal joints of each finger respectively and 1 stretchable resistor distributed at the purlicue. The e-skin sensor can be attached to the opisthenar, and thanks to its stretchability, the sensor can detect the bent angle of the finger. The customized FPC, with WiFi module, wirelessly transmits the signal to the terminal device with human-machine interface, and we design a graphical user interface based on the Qt framework for real-time signal acquisition, storage, and display. Based on this developed e-skin system and self-developed robotic multi-fingered hand, we conduct gesture recognition and robotic multi-fingered teleoperation experiments using deep learning techniques and obtain a recognition accuracy of 91.22%. The results demonstrate that the developed e-skin sensing system has great potential in human-machine interaction.
科研通智能强力驱动
Strongly Powered by AbleSci AI