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<p>Traditional multimodal flexible sensors struggle with system integration, limited node scalability, and overall robustness, posing multiple critical challenges. Inspired by the tiger-shark scalp, we present BDMFS, a&#xa0;robust bionic distributed multimodal flexible sensor, integrating an S-shaped optical network mimicking subcutaneous mechanoreceptors with a self-powered triboelectric interface emulating ampullae-based proximity sensing. A microstructured elastic dielectric layer serves as both optical substrate and triboelectric layer, providing exceptional flexibility, mechanical robustness, and environmental adaptability under diverse conditions. BDMFS enables spatiotemporally synchronized perception of proximity (~ 100 mm) and tactile (~ 5 ms) stimuli, detecting gentle touches of 0.25 g while withstanding 6.26 MPa pressures. Coupled with machine-learning, it achieves 95.26% object-proximity recognition accuracy, demonstrated in real-time virtual music teaching, adaptive grasping under low light, and wrist-mounted underwater teleoperation, highlighting its potential for intelligent control and advanced human-robot interaction in extreme environments.</p>

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Robust bionic distributed multimodal flexible sensor for extreme-condition sensing and intelligent operation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月2日

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  • Baijin Mao,
  • Yedong Huang,
  • Yuyaocen Xiang,
  • Wenbo Liu,
  • Xunlong Shi,
  • Xiang Qian,
  • Juntian Qu

摘要

Traditional multimodal flexible sensors struggle with system integration, limited node scalability, and overall robustness, posing multiple critical challenges. Inspired by the tiger-shark scalp, we present BDMFS, a robust bionic distributed multimodal flexible sensor, integrating an S-shaped optical network mimicking subcutaneous mechanoreceptors with a self-powered triboelectric interface emulating ampullae-based proximity sensing. A microstructured elastic dielectric layer serves as both optical substrate and triboelectric layer, providing exceptional flexibility, mechanical robustness, and environmental adaptability under diverse conditions. BDMFS enables spatiotemporally synchronized perception of proximity (~ 100 mm) and tactile (~ 5 ms) stimuli, detecting gentle touches of 0.25 g while withstanding 6.26 MPa pressures. Coupled with machine-learning, it achieves 95.26% object-proximity recognition accuracy, demonstrated in real-time virtual music teaching, adaptive grasping under low light, and wrist-mounted underwater teleoperation, highlighting its potential for intelligent control and advanced human-robot interaction in extreme environments.