Recent advancements toward perception and decision-making of flexible endoscopes have shown great potential in computer-aided surgical interventions. However, owing to modeling uncertainty and inter-patient anatomical variation in flexible endoscopy, the challenge remains for efficient and safe navigation in patient-specific scenarios. This paper presents a novel data-driven framework with self-contained visual-shape fusion for autonomous intelligent navigation of flexible endoscopes requiring no priori knowledge of system models and global environments. A learning-based adaptive visual servoing controller is proposed to online update the eye-in-hand vision-motor configuration and steer the endoscope, which is guided by monocular depth estimation via a vision transformer (ViT). To prevent unnecessary and excessive interactions with surrounding anatomy, an energy-motivated shape planning algorithm is introduced through entire endoscope 3-D proprioception from embedded fiber Bragg grating (FBG) sensors. Furthermore, a model predictive control (MPC) strategy is developed to minimize the elastic potential energy flow and simultaneously optimize the steering policy. Dedicated navigation experiments on a robotic-assisted flexible endoscope with an FBG fiber in several phantom environments demonstrate the effectiveness and adaptability of the proposed framework.

翻译：近年来，柔性内窥镜感知与决策技术的进步在计算机辅助外科干预中展现出巨大潜力。然而，由于柔性内窥镜操作中存在建模不确定性与患者间解剖结构差异，如何在个体化手术场景中实现高效安全的导航仍具挑战。本文提出一种新颖的数据驱动框架，通过自包含的视觉-形状融合技术实现柔性内窥镜的自主智能导航，该框架无需系统模型及全局环境的先验知识。本研究设计了一种基于学习的自适应视觉伺服控制器，可在线更新眼-手视觉-运动配置并引导内窥镜运动，其导航策略由基于视觉transformer（ViT）的单目深度估计驱动。为避免与周围解剖结构发生非必要及过度接触，本文通过嵌入式光纤布拉格光栅（FBG）传感器获取内窥镜三维本体感知信息，引入基于能量激励的形状规划算法。进一步，提出模型预测控制（MPC）策略以最小化弹性势能流并同步优化转向策略。在配备FBG光纤的机器人辅助柔性内窥镜系统中，于多个仿真体模环境中开展的专项导航实验验证了所提框架的有效性与适应性。