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用于超分辨率触觉传感且具有力自解耦功能的柔性磁性皮肤

Soft magnetic skin for super-resolution tactile sensing with force self-decoupling.

作者信息

Yan Youcan, Hu Zhe, Yang Zhengbao, Yuan Wenzhen, Song Chaoyang, Pan Jia, Shen Yajing

机构信息

Department of Biomedical Engineering, City University of Hong Kong, Hong Kong SAR, China.

Department of Computer Science, University of Hong Kong, Hong Kong SAR, China.

出版信息

Sci Robot. 2021 Feb 24;6(51). doi: 10.1126/scirobotics.abc8801.

DOI:10.1126/scirobotics.abc8801
PMID:34043530
Abstract

Human skin can sense subtle changes of both normal and shear forces (i.e., self-decoupled) and perceive stimuli with finer resolution than the average spacing between mechanoreceptors (i.e., super-resolved). By contrast, existing tactile sensors for robotic applications are inferior, lacking accurate force decoupling and proper spatial resolution at the same time. Here, we present a soft tactile sensor with self-decoupling and super-resolution abilities by designing a sinusoidally magnetized flexible film (with the thickness ~0.5 millimeters), whose deformation can be detected by a Hall sensor according to the change of magnetic flux densities under external forces. The sensor can accurately measure the normal force and the shear force (demonstrated in one dimension) with a single unit and achieve a 60-fold super-resolved accuracy enhanced by deep learning. By mounting our sensor at the fingertip of a robotic gripper, we show that robots can accomplish challenging tasks such as stably grasping fragile objects under external disturbance and threading a needle via teleoperation. This research provides new insight into tactile sensor design and could be beneficial to various applications in robotics field, such as adaptive grasping, dexterous manipulation, and human-robot interaction.

摘要

人类皮肤能够感知法向力和剪切力(即自解耦)的细微变化,并且能够以比机械感受器平均间距更精细的分辨率感知刺激(即超分辨)。相比之下,现有的用于机器人应用的触觉传感器则较为逊色,同时缺乏精确的力解耦和适当的空间分辨率。在此,我们通过设计一种正弦磁化的柔性薄膜(厚度约为0.5毫米),提出了一种具有自解耦和超分辨能力的柔性触觉传感器,其变形可通过霍尔传感器根据外力作用下磁通密度的变化进行检测。该传感器能够用单个单元精确测量法向力和剪切力(在一维中得到证明),并通过深度学习实现了60倍的超分辨精度提升。通过将我们的传感器安装在机器人夹具的指尖上,我们展示了机器人能够完成具有挑战性的任务,例如在外部干扰下稳定抓取易碎物体以及通过遥操作穿针引线。这项研究为触觉传感器设计提供了新的见解,并且可能有益于机器人领域的各种应用,如自适应抓取、灵巧操作和人机交互。

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