Multi-Interface Strain Transfer Modeling for Flexible Endoscope Shape Sensing

• Published in: IEEE robotics and automation letters Vol. 9; no. 3; pp. 2670 - 2677
• Main Authors: Liu, Xinran, Chen, Jian, Hu, Jian, Chen, Hao, Huang, Yuanrui, Liu, Hongbin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-03-2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Angles (geometry); Diameters; Endoscopes; Endoscopy; Fiber optics; Optical fiber sensors; Optical fibers; Robot sensing systems; Robotic surgery; Sensors; Shape; Slenderness ratio; soft sensors and actuators; Strain; Strain measurement; Surgical robotics: laparoscopy; surgical robotics: steerable catheters/needles; Three dimensional models
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2024.3359546

Robot-assisted minimally invasive surgery (MIS) using flexible endoscopy has emerged as a groundbreaking technology for improving traditional surgical approaches. However, a major challenge in advancing this technology is the lack of shape sensing, which leads to inaccurate navigation and control of ultra-long flexible endoscopy within the narrow and tortuous lumen environment. The unique characteristics of flexible endoscopy, including large slenderness ratio, high bending angles, and non-symmetric, multi-channel configurations, pose significant challenges to accurate shape sensing. To address this challenge, we propose a novel shape-sensing scheme based on distributed fiber optic strain measurement, which incorporates a complete and applicable multi-interface strain transfer model adapted to large deformation and multiple sensing points. To validate the theoretical model, a shape sensor with a diameter of 2.84 mm and a length of 500 mm is fabricated. Both 2D and 3D shape sensing experiments are conducted on predefined templates, and the results highlight a significant improvement in the precision of sensor measurements through the utilization of the proposed model. Specifically, the 3D experiment results show a mean absolute error (MAE) of 5.38 mm for complex geometrical shapes and the proposed model reduces the MAE by approximately 52.9% compared to the unmodified case.