Robust and Flexible Sliding Tactile Sensor for Surface Pattern Perception and Recognition

• Published in: Advanced intelligent systems Vol. 5; no. 10
• Main Authors: Zhu, Zhihao, Xu, Yingtian, Lin, Waner, Hu, Zhixian, Lin, Zhuofan, Sun, Zhenglong, Peng, Zhengchun, Wang, Ziya
• Format: Journal Article
• Language: English
• Published: Weinheim John Wiley & Sons, Inc 01.10.2023; Wiley
• Subjects: Cambering; Data acquisition; Decision making; Grooves; Machine learning; Object recognition; Pattern recognition; Perception; Pressure distribution; Robots; Robustness; Sensors; Sliding; sliding perception; Surface properties; Tactile discrimination; tactile sensors; Tactile sensors (robotics)
• ISSN: 2640-4567; 2640-4567
• DOI: 10.1002/aisy.202300225

Perceiving surface characteristics through tactile interaction typically requires high‐resolution devices or precise spatial scanning to record and analyze a significant amount of information. However, most available tactile sensors require complicated technological processes, redundant layouts, and data acquisition circuits, which limits their ability to provide a real‐time static perception and feedback for potential applications such as robotic manipulation. Drawing inspiration from the sliding tactile (ST) perception mode of the human fingertip, a robust and flexible ST sensor with a low array density of 2.7 cells cm−2 is reported. This innovative sensor has a soft and cambered configuration that allows it to rapidly and accurately recognize the 3D surface features of objects, including grooves as small as 500 μm. Benefiting from the strong correlation between collected electronic responding and local deformation of sensing cell, the ST sensor can adaptively reconstruct surface patterns with the assistance of deep learning, even on unstructured objects. The pattern recognition system based on the robot is demonstrated by accurately classifying a set of mahjong tiles with nearly 100% accuracy, surpassing human tactile perception capabilities in the same task. Herein, a flexible sliding tactile sensor that is inspired by the human fingertip, capable of constructing high‐resolution pattern images using just a few sensor cells, is proposed. Its unique deformation‐induced sensing mechanism provides a strongly correlated static response to unstructured pattern features and applied pressure. When combined with deep learning, this technology enables a robot to automatically classify significant patterns.