A Rigid-Flexible Coupled Six-Dimensional Force Sensor and Its PINN-Based Decoupling Algorithm.

Yinlong Zhu, Zhengyu Xie, Chuanwei Lu, Shuang Xi, Xu Wang

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Yinlong ZhuCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.ORCID 0000-0003-4574-3900

Zhengyu XieCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.ORCID 0009-0008-4236-2866

Chuanwei LuCollege of Chemical Engineering, Nanjing Forestry University, Nanjing 210037, China.ORCID 0000-0003-2396-3208

Shuang XiCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.ORCID 0000-0001-5018-1987

Xu WangCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.ORCID 0000-0002-0680-6908

Funding

High-Level Talent Project of "Six Talent Peaks" in Jiangsu Province GDZB-024Jiangsu Government Scholarship for Overseas Studies JS-2016-62 and JS-2024-7National Natural Science Foundation of China 51305209Natural Science Research Projects in Jiangsu Universities 18KJA4600050Natural Science Research Projects in Jiangsu Universities 21KJB460010Open Project of the National Key Laboratory of Robotics 2018-016

6 · The paper itself

Abstract

Six-dimensional force sensors are widely used in compliant robotic control and safe human-machine interactions due to their mature sensing mechanisms and high accuracy. However, conventional six-dimensional force sensors often suffer from complex structures, bulky size, and high manufacturing costs. To address these limitations, this paper proposes a compact and low-cost six-axis force sensor based on capacitive sensing. By employing a tailored arrangement of flexible sensing units, partial structural decoupling of force and torque in specific directions is achieved. A Physically Informed Neural Network (PINN) is further introduced to decouple the residual coupled signals. Experimental results demonstrate that the proposed method significantly improves decoupling accuracy, achieving force decoupling errors of 1.75%, 1.20%, and 1.31% for

Indexed as

PINNsensor decoupling algorithmsix-dimensional force sensorstructural decoupling

Identifiers

PMID41977825

DOI10.3390/s26072038

PMCPMC13074577

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.