Data-driven multi- joint waveguide bending sensor based on time series neural network

Due to the bulky interrogation devices, traditional fiber optic sensing system is mainly connected by wire or equipped only for large facilities. However, the advancement in neural network algorithms and flexible materials has broadened its application scenarios to bionics. In this paper, a multi-joint waveguide bending sensor based on color dyed filters is designed to detect bending angles, directions and positions. The sensors are fabricated by casting method using soft silicone rubber. Besides, required optical properties of sensor materials are characterized to better understand principles of the sensor design. Time series neural networks are utilized to predict bending position and angle quantitatively. The results confirm that the waveguide sensor demodulated by the data-driven neural network algorithm performs well and can be used for engineering applications. (c) 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Automated summary

In this paper, a multi-joint waveguide bending sensor based on color dyed filters is designed to detect bending angles, directions and positions. The sensors are fabricated using soft silicone rubber and time series neural networks are utilized for quantitative prediction. The results confirm the good performance of the sensor for engineering applications.