Fiber-Optic Shape Sensing With Nonlinear Twist Compensation Using Large-Pitch Helical Multicore Fibers

Abstract

A novel nonlinear twist compensation employed fiber-optic shape sensing method is proposed in this work. Although it has been demonstrated that the helical multicore fiber (HMCF) is applicable for accurate shape sensing, including bending and twist, the nonlinear relationship between the external twist and the induced strain is still an issue that will cause significant errors in shape reconstructions. To address this issue, a nonlinear twist compensation approach is applied to the helical extension method for high-accuracy shape sensing when arbitrary twists occur along the HM CF. For validation, a simulation based on the rotated minimal frame was conducted to generate strain distributions of HMCF under various bending and twist conditions. Then, the 3-D shape of the sensing fiber was reconstructed by using the Frenet-Serret method with linear compensation, the Foris method, as well as the proposed nonlinear method. In the analysis of the influences of target shape, helical pitch of the HMCF, external twist rate, and sample points to the reconstruction error, the proposed method has shown the best performances, compared to other twist compensation methods, including the linear and Foris methods. In addition, the shape sensing results in the coronary artery intervention pathway demonstrated that the proposed method effectively mitigates the nonlinear effects and significantly enhances the adaptability of large-pitch (up to 200 mm) HMCF in shape sensing. Experimental validation was also conducted and achieved a 5.30-mm maximum 3-D shape reconstruction error.