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超拉伸分段传感器,用于功能型人机交互界面。

Ultrastretchable Segmented Sensors for Functional Human-Machine Interfaces.

机构信息

School of Physics, Engineering and Technology, University of York, York YO10 5DD, U.K.

School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, South Korea.

出版信息

ACS Appl Mater Interfaces. 2024 Jun 26;16(25):32784-32793. doi: 10.1021/acsami.4c08547. Epub 2024 Jun 11.

Abstract

The key feature that enables soft sensors to shorten the performance gap between robots and biological structures is their deformability, coupled with their capability to measure physical changes. Robots equipped with these sensors can interact safely and proprioceptively with their environments. This has sparked interest in developing novel sensors with high stretchability for application in human-robot interactions. This study presents a novel ultrasoft optoelectronic segmented sensor design capable of measuring strains exceeding 500%. The sensor features an ultrastretchable segment physically joined with an asymmetrically configured soft proprioceptive segment. This configuration enables it to measure high strain and to detect both the magnitude and direction of bending. Although the sensor cannot decouple these types of deformations, it can sense prescribed motions that combine stretching and bending. The proposed sensor was applied to a highly deformable scissor mechanism and a human-robot interface (HRI) device for a robotic arm, capable of quantifying parameters in complex interactions. The results from the experiments also demonstrate the potential of the proposed segmented sensor concept when used in tandem with machine learning, affording new dimensions of proprioception to robots during multilayered interactions with humans.

摘要

使软传感器能够缩短机器人和生物结构之间性能差距的关键特征是它们的可变形性,以及它们测量物理变化的能力。配备这些传感器的机器人可以与环境安全地、 proprioceptively 交互。这激发了人们开发具有高拉伸性的新型传感器的兴趣,以便应用于人机交互。本研究提出了一种新颖的超软光电分段传感器设计,能够测量超过 500%的应变。该传感器具有超可拉伸的分段,物理上与不对称配置的软本体感觉分段相连。这种配置使其能够测量高应变,并检测弯曲的大小和方向。虽然传感器不能解耦这些类型的变形,但它可以感知组合拉伸和弯曲的规定运动。所提出的传感器被应用于高度可变形的剪刀机构和用于机器人臂的人机界面 (HRI) 设备,能够量化复杂相互作用中的参数。实验结果还表明,当与机器学习结合使用时,所提出的分段传感器概念具有潜力,为机器人在与人类的多层交互中提供了新的本体感觉维度。

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