Surface Topography Perception Method Based on Fiber Optic Tactile Sensing

Tactile perception represents a fundamental sensory modality through which humans engage with the external environment. In virtual and augmented reality (VR/AR) technologies, users can achieve a more immersive and verisimilar interactive experience by perceiving the surface topography of contacted objects. This study addresses the bionic requirements for tactile sensors to identify surface topography features primarily characterized by hardness and surface flatness. We present a novel tactile feature perception and classification method integrating fiber Bragg grating (FBG) tactile sensing technology with deep learning architectures. A three-point synchronous tactile sensor model is employed to collect data from objects with diverse hardness and flatness attributes. Static stress and dynamic vibration signals are extracted via wavelength-swept optical coherence tomography technology, facilitating the construction of a three-point static-dynamic tactile dataset. Based on this dataset, a gated Transformer-based deep learning network is designed to classify 20 distinct combinations of tactile features, encompassing four levels of Shore A hardness and five surface flatness variations. The proposed method yields an average classification accuracy of 93.756% with a single recognition time of approximately 1.621 ms, validating the feasibility of integrating tactile sensing with deep learning networks. This approach offers an efficient solution for biomimetic, real-time, and high-precision tactile perception, demonstrating significant potential for practical applications.