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<p>Electronic artificial skin (E-skin) replicates human tactile sensations with exceptional sensitivity and accuracy, enabling the detection of physical properties, including the shape, material, and texture of objects. Current technologies effectively detect slippage on dry surfaces but not on oil- or water-coated wet surfaces. This paper presents a wearable slip sensor featuring a micropatterned structure inspired by human fingerprints, capable of detecting slippage under all surface wetness conditions. The proposed sensor incorporates a randomly patterned fingerprint design, laser-etched onto the topmost layer of a multilayer film. It effectively detects surface slippage, even on oil film-coated low-friction surfaces. Additionally, the sensor captures intricate geometric features of microtextures, including microvibrations and ultrafast signal changes. Its applicability in soft robotic hands is demonstrated by its high-speed detection of the sliding motion of various objects. The findings will aid in advancing digital-on-demand technologies by enabling the precise reconstruction of digital tactile data within cyber-physical systems.</p>

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AI-integrated bionic fingertip E-Skin for precision slippage detection in wet environments

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月19日

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  • Tsubasa Adachi,
  • Koki Ozawa,
  • Shoma Kamanoi,
  • Junya Yoshida,
  • Riku Sasaki,
  • Yasuyuki Miura,
  • Yoshihito Takabe,
  • Fabrice Domingues Dos Santos,
  • Tong Huang,
  • Atsushi Miyabo,
  • Yasunori Takeda,
  • Hiroyuki Matsui,
  • Tomohito Sekine

摘要

Electronic artificial skin (E-skin) replicates human tactile sensations with exceptional sensitivity and accuracy, enabling the detection of physical properties, including the shape, material, and texture of objects. Current technologies effectively detect slippage on dry surfaces but not on oil- or water-coated wet surfaces. This paper presents a wearable slip sensor featuring a micropatterned structure inspired by human fingerprints, capable of detecting slippage under all surface wetness conditions. The proposed sensor incorporates a randomly patterned fingerprint design, laser-etched onto the topmost layer of a multilayer film. It effectively detects surface slippage, even on oil film-coated low-friction surfaces. Additionally, the sensor captures intricate geometric features of microtextures, including microvibrations and ultrafast signal changes. Its applicability in soft robotic hands is demonstrated by its high-speed detection of the sliding motion of various objects. The findings will aid in advancing digital-on-demand technologies by enabling the precise reconstruction of digital tactile data within cyber-physical systems.