灵敏度(控制系统)
机器人学
人工智能
电容
解耦(概率)
拓扑(电路)
接口(物质)
计算机科学
算法
机器人
电气工程
电子工程
工程类
物理
控制工程
电极
气泡
量子力学
最大气泡压力法
并行计算
作者
D. Wang,Ningjuan Zhao,Zekun Yang,Yangbo Yuan,Hongcheng Xu,Guirong Wu,Weihao Zheng,Xiangrui Ji,Ningning Bai,Weidong Wang,Chenyang Xue,Libo Gao
标识
DOI:10.1109/led.2023.3324086
摘要
Flexible three-dimensional (3D) force sensors have been extensively investigated in the field of robotics due to their ability to provide feedback information from multiple directions. However, the development of flexible 3D force sensors with high sensitivity and decoupling capabilities remains a significant challenge, hindering the ability of robots to perceive their external environment. In this letter, we present a novel flexible 3D force iontronic sensor (FTIS) that utilizes ionic materials with micro-pyramidal structures and a backpropagation (BP) neural network method based on deep learning. The FTIS exhibits outstanding sensitivity, with over 8000 $\text{N}^{-{1}}$ in the normal direction and over 4000 $\text{N}^{-{1}}$ in the shear direction, and has a rapid response time of 27-ms. Additionally, it demonstrated stable working durability, with over 8000 cycles without signal delay. To validate the utility of the sensor, we integrated it as a machine-sensing interface on a mechanical claw to measure changes in forces in the triaxial direction. Our design concept has the potential to advance the development of multidimensional force sensors in the future.
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