Design of 3D Magnetic Tactile Sensors with High Sensing Accuracy Guided by the Theoretical Model

被引:7
作者
Hu, Xiaocheng [1 ]
Zhu, Heng [1 ]
Chen, Ruiwen [2 ]
Hu, Sideng [2 ]
Jia, Zheng [1 ]
Yu, Honghui [3 ]
Qu, Shaoxing [1 ]
机构
[1] Zhejiang Univ, State Key Lab Fluid Power & Mechatron Syst, Key Lab Soft Machines & Smart Devices Zhejiang P, Eye Ctr,Affiliated Hosp 2,Ctr Mech X,Dept Engn Me, Hangzhou 310027, Peoples R China
[2] Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Peoples R China
[3] CUNY City Coll, Dept Mech Engn, New York, NY 10031 USA
基金
中国国家自然科学基金;
关键词
artificial neural networks; magnetic tactile sensor; nonlinear magnetic flux density; nonlinear solid mechanics; SKIN;
D O I
10.1002/aisy.202200291
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The past decade has witnessed a surging interest in the study of magnetic tactile sensors that can detect subtle changes in both normal and shear forces. However, due to the lack of guidance by appropriate theoretical models, the development of previous magnetic tactile sensors relies either on a trial-and-error manner or tedious point-by-point experimental calibrations, which are costly and time-inefficient. Here, a theoretical model integrating magnetics, artificial neural networks, and nonlinear solid mechanics is proposed for the first time to guide the design of 3D magnetic tactile sensors. Then, a button-shaped magnetic tactile sensor prototype that can detect subtle triaxial force changes is fabricated, which relates the nonlinear magnetic flux density to the external force, without burdensome calibration procedures. The sensor can achieve an axial measurement error of less than 1% and an in-plane error of less than 3.7% with excellent durability. This study provides a comprehensive understanding of magnetic tactile sensors and sheds light on their applications in soft robotics, intelligent manipulation, and human-robot interaction (HRI).
引用
收藏
页数:12
相关论文
共 28 条
  • [1] Ananthanarayanan A, 2012, IEEE INT CONF ROBOT, P1398, DOI 10.1109/ICRA.2012.6225201
  • [2] Experiments and modeling of the viscoelastic behavior of polymeric gels
    Bosnjak, Nikola
    Nadimpalli, Siva
    Okumura, Dai
    Chester, Shawn A.
    [J]. JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS, 2020, 137
  • [3] Chao Hu, 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, P628
  • [4] De Angelis G., 2017, SENSORS-BASEL, V17, P1
  • [5] Design Optimisation of a Magnetic Field Based Soft Tactile Sensor
    de Boer, Gregory
    Raske, Nicholas
    Wang, Hongbo
    Kow, Junwai
    Alazmani, Ali
    Ghajari, Mazdak
    Culmer, Peter
    Hewson, Robert
    [J]. SENSORS, 2017, 17 (11):
  • [6] Cylindrical magnets and ideal solenoids
    Derby, Norman
    Olbert, Stanislaw
    [J]. AMERICAN JOURNAL OF PHYSICS, 2010, 78 (03) : 229 - 235
  • [7] A Magnetic Type Tactile Sensor by GMR elements and inductors
    Goka, Masanori
    Nakamoto, Hiroyuki
    Takenawa, Satoru
    [J]. IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010), 2010, : 885 - 890
  • [8] Soft Magnetic Tactile Skin for Continuous Force and Location Estimation Using Neural Networks
    Hellebrekers, Tess
    Chang, Nadine
    Chin, Keene
    Ford, Michael J.
    Kroemer, Oliver
    Majidi, Cannel
    [J]. IEEE ROBOTICS AND AUTOMATION LETTERS, 2020, 5 (03) : 3892 - 3898
  • [9] Hu C, 2006, WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS, P5304
  • [10] Hu C, 2005, P ANN INT IEEE EMBS, P7143