Data-Driven Control of Highly Deformable Robotic Endoscopes Using Proprioceptive Shape Feedback

作者：Yiang Lu, Jixuan Zheng, Yun-Hui Liu · 年份：2024 · DOI：10.1109/robio64047.2024.10907478 · 研究领域：Soft Robotics and Applications、Modular Robots and Swarm Intelligence

In this paper, we introduce a versatile data-driven approach for servo-controlling the highly deformable robotic endoscope equipped with Draw Tower Gratings (DTGs). The advancement of perception and decision-making technologies is pivotal for enabling flexible robots to execute automatic procedures. However, modeling these robots presents a significant challenge due to their large deformations and susceptibility to environmental disturbances. To address this issue, we propose a robust adaptive control framework that leverages DTG-based proprioceptive shape feedback. This automatic paradigm can online estimate the unknown deformation model of the flexible robot based on function approximation (FA), while also accounting for unknown perturbations without prior identification. By employing the proposed FA-based adaptive algorithm, we can ensure the convergence of the shape control error. Experimental results validating a highly deformable flexible robot embedded with DTG sensors showcase the efficiency of the proposed control strategy in unknown scenarios.