Highly Sensitive Linear Triaxial Force Sensor Based on Multimodal Sensing for 3D Pose Reconstruction.

IF 10.7 2区 材料科学 Q1 CHEMISTRY, PHYSICAL
Yongwei Zhang, Jaafar Abdul-Aziz Mehrez, Jianhua Yang, Wangze Ni, Chao Fan, Wenjing Quan, Kai Zhang, Tao Wang, Min Zeng, Nantao Hu, Zhi Yang
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引用次数: 0

Abstract

Flexible sensing offers real-time force monitoring, presenting a versatile and effective solution for dexterous manipulation, healthcare, environmental exploration, and perception of physical properties. Nonetheless, a limitation of many existing flexible force sensors stems from their isotropic structure or material properties, preventing them from simultaneously detecting both the direction and magnitude of the applied force. Herein, a high-performance 3D force sensor based on orthogonal multimodal sensing, the cancellation principle, and the strain effect is proposed. Finite element analysis further reveals the decoupling and anti-interference mechanisms of the innovative capacitor-resistance dual-mode sensing based on a solid mechanics and electrostatic multiphysics model. The sensor demonstrates the ability to measure both the magnitude and direction of normal and shear forces in any combination using the proposed decoupling and reconstruction algorithms, showing the potential for accurately reconstructing the posture of objects.

基于多模态传感的高灵敏度线性三轴力传感器用于三维位姿重建。
柔性传感可提供实时力监测,为灵巧操作、医疗保健、环境探索和物理特性感知提供了多功能的有效解决方案。然而,许多现有柔性力传感器的局限性在于其各向同性的结构或材料特性,无法同时检测作用力的方向和大小。本文提出了一种基于正交多模态传感、抵消原理和应变效应的高性能三维力传感器。有限元分析进一步揭示了基于固体力学和静电多物理场模型的创新电容电阻双模传感的去耦合和抗干扰机制。该传感器展示了利用所提出的去耦和重建算法测量任意组合的法向力和剪切力的大小和方向的能力,显示了准确重建物体姿态的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Small Methods
Small Methods Materials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍: Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques. With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community. The online ISSN for Small Methods is 2366-9608.
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