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Active-Model-Based Precise Twist Steering for Autonomous Robotic Flexible Endoscope

Xiangyu Wang, Chong Liu, Yongchun Fang, Ningbo Yu, Yanding Qin, Hongpeng Wang, Jianda Han

Year
2025
Citations
2

Abstract

In natural orifice transluminal endoscopic surgery (NOTES), the twist steering of the flexible endoscope plays an important role in tracking the preoperative path during cavity intervention. However, the flexible endoscope’s twisting motion has high nonlinearity and uncertainty, which bring challenges for accurate modeling and controller design. In this study, a novel active modeling-based improved control (AMIC) scheme is proposed, which achieves precise control of the robotic flexible endoscope’s twisting motion. First, the Coleman-Hodgdon (C-H) model is modified to serve as the reference model to describe the twist steering. Then, the model error in the C-H model is introduced as an extended state. Upon this, an active modeling algorithm is developed by using the unscented Kalman filter. The proposed model estimates both the twisting angle and the model error in real time. Based on the proposed model, the AMIC strategy is developed to enhance the tracking performance of a proportional-integral-derivative (PID) controller for a reference trajectory. Finally, comparative experiments were conducted on a self-built robotic flexible endoscope under various insertion depths and tip-part configurations. Compared to the PID controller, the experimental results demonstrate that the proposed AMIC scheme achieves a 63.1% reduction in tracking error under a sinusoidal trajectory.

Keywords

TwistComputer scienceEndoscopeActive steeringControl engineeringArtificial intelligenceSimulationEngineeringControl (management)Mathematics

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