Advances in fiber-optic-based 3D shape sensing technology

Fiber-optic 3D shape sensing technology, renowned for its immunity to electromagnetic interference and unparalleled spatial accuracy, is indispensable for real-time deformation monitoring in demanding and intricate environments. This study delves into the sensing mechanisms of both multi-core fibers (MCFs) and fiber bundle configurations. It examines quasi-distributed sensing approaches, including fiber Bragg gratings (FBGs), and addresses mitigation techniques for temperature-strain cross-sensitivity. A comparative analysis of distributed sensing methods, such as Brillouin and Rayleigh scattering, is presented, emphasizing their distinct performance characteristics. Furthermore, the paper systematically outlines shape sensing algorithms, focusing on curvature and shape reconstruction, and explores torsion demodulation techniques, underscoring the transformative impact of machine learning in enhancing reconstruction precision and computational efficiency. Lastly, it highlights critical challenges in distributed fiber-optic shape sensing (DFOSS) and outlines future research trajectories, providing a holistic overview of the technology’s current landscape and its potential for groundbreaking advancements.