Self-Powered Multimodal Tactile Sensing Enabled by Hybrid Triboelectric and Magnetoelastic Mechanisms.

Journal: Cyborg and bionic systems (Washington, D.C.)
Published Date: Jul 2, 2025

Abstract

Object property perception, as a core component of tactile sensing technology, faces severe challenges due to its inherent complexity and diversity, particularly under the constraints of decoupling difficulty and limited precision. Herein, this paper introduces an innovative approach to object property perception utilizing triboelectric-magnetoelastic sensing. This technology integrates triboelectricity and magnetoelasticity, achieving a self-powered sensing mechanism that requires no external power source for sensing signal generation. Moreover, by deploying a triboelectric array, it comprehensively captures multi-dimensional information of objects. Concurrently, in conjunction with magnetoelastic sensing technology, it provides stable and reliable mechanical information, ensuring that the system can accurately decouple key characteristics of objects, such as material properties, softness, and roughness, even in open environments where temperature, humidity, and mechanical conditions change in real time. Furthermore, by combining deep learning algorithms, it achieves exceptionally high recognition accuracy for object properties (material recognition accuracy: 99%, softness recognition accuracy: 100%, roughness recognition accuracy: 95%). Even in complex scenarios with intertwined multiple properties, the overall recognition accuracy remains consistently above 95%. The multimodal tactile sensing technology proposed in this paper provides robust technical support and theoretical foundation for the intelligent development of robots and the enhancement of real-time tactile perception capabilities.

Authors

  • Xiao Lu
    School of Computer Science and Engineering, University of Electronic Science and Technology of China, Sichuan, 611731, China.
  • Tianhong Wang
    Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China.
  • Songyi Zhong
    School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China.
  • Tianqi Cao
    Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, China.
  • Chenghao Zhou
    School of Chinese Materia Medica, Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Long Li
    Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China.
  • Quan Zhang
    Department of Pulmonary and Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.
  • Shiwei Tian
    School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China.
  • Tao Jin
  • Tao Yue
    Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX 75390.
  • Shaorong Xie

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