A Bionic Textile Sensory System for Humanoid Robots Capable of Intelligent Texture Recognition.

Journal: Advanced materials (Deerfield Beach, Fla.)
Published Date:

Abstract

Artificial tactile perception systems that emulate the functions of slow adaptive (SA) and fast adaptive (FA) cutaneous mechanoreceptors are essential for developing advanced prosthetics and humanoid robots. However, constructing a high-performance sensory system within a single device capable of simultaneously perceiving both static and dynamic forces for surface-texture recognition remains a critical challenge; this contrasts with common strategies integrating individual SA- and FA-mimicking sensors in multi-layered, multi-circuit configurations. Herein, a textile pressure/tactile (PT) sensor is reported based solely on piezoresistive principle alongside high sensitivity and rapid response to both high-frequency vibrations and static forces. These characteristics are attributed to the sensor's 3D multiscale architecture and the corresponding hierarchical structural deformation of its honeycomb-like sensing fabric. As a proof-of-concept application relevant to humanoid robotics and prosthetics, an automated surface-texture-recognition system is constructed by integrating the PT sensor with machine-learning algorithms, a prosthetic device, an industrial robot arm, and a graphical user interface. This artificial sensory system demonstrates the ability to learn distinct object features, differentiate fine surface textures, and subsequently classify unknown textiles with high recognition accuracy (>98.9%) across a wide range of scanning speeds (50-300 mm s). These results show promise for the future development of interactive artificial intelligence.

Authors

  • Xianhong Zheng
    School of Textile and Garment, Anhui Polytechnic University, Wuhu, 241000, China.
  • Runrun Zhang
    Institute of Advanced Materials (IAM), Nanjing Tech University (NanjingTech), Nanjing, 211816, China.
  • Binbin Ding
    School of Textile and Garment, Anhui Polytechnic University, Wuhu, 241000, China.
  • Zhao Zhang
  • Yu Shi
    NIH BD2K Program Centers of Excellence for Big Data Computing-KnowEng Center, Department of Computer Science, University of Illinois at Urbana-Champaign , Champaign, Illinois.
  • Leang Yin
    Institute of Advanced Materials (IAM), Nanjing Tech University (NanjingTech), Nanjing, 211816, China.
  • Wentao Cao
    Department of Urology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
  • Zongqian Wang
    School of Textile and Garment, Anhui Polytechnic University, Wuhu, 241000, China.
  • Guiyang Li
    School of Textile and Garment, Anhui Polytechnic University, Wuhu, 241000, China.
  • Zhi Liu
  • Changlong Li
    School of Textile and Garment, Anhui Polytechnic University, Wuhu, 241000, China.
  • Zunfeng Liu
  • Wei Huang
    Shaanxi Institute of Flexible Electronics, Northwestern Polytechnical University, 710072 Xi'an, China.
  • Gengzhi Sun
    Institute of Advanced Materials (IAM) , Nanjing Tech University (NanjingTech) , 30 South Puzhu Road , Nanjing 211816 , People's Republic of China.

Keywords

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