Artificial tactile perception smart finger for material identification based on triboelectric sensing

TLDR

Tactile perception involves both direct sensory responses and psychological processing, yet accurately quantifying texture and roughness through artificial haptic sensing remains difficult. The study develops a smart finger that surpasses human tactile perception to accurately identify material type and roughness using triboelectric sensing and machine learning. The device employs a triboelectric sensor array that generates material‑specific electron‑transfer fingerprints, which are analyzed by machine‑learning algorithms to classify material type and roughness. The triboelectric sensor array achieved a 96.8% accuracy in material identification and demonstrates potential for equipping robotic manipulators or prosthetics with artificial tactile perception.

Abstract

Tactile perception includes the direct response of tactile corpuscles to environmental stimuli and psychological parameters associated with brain recognition. To date, several artificial haptic-based sensing techniques can accurately measure physical stimuli. However, quantifying the psychological parameters of tactile perception to achieve texture and roughness identification remains challenging. Here, we developed a smart finger with surpassed human tactile perception, which enabled accurate identification of material type and roughness through the integration of triboelectric sensing and machine learning. In principle, as each material has different capabilities to gain or lose electrons, a unique triboelectric fingerprint output will be generated when the triboelectric sensor is in contact with the measured object. The construction of a triboelectric sensor array could further eliminate interference from the environment, and the accuracy rate of material identification was as high as 96.8%. The proposed smart finger provides the possibility to impart artificial tactile perception to manipulators or prosthetics.