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：最近对灵活内窥镜的认知和决策进展在计算机辅助外科手术干预方面显示出巨大的潜力,然而,由于在灵活的内窥镜中模拟不确定性和病人间解剖变化,在病人特定情况中高效和安全导航方面仍然存在挑战。本文件提出一个新的数据驱动框架,其中包含一个自成一体的视觉成像组合,用于自动智能导航需要系统模型和全球环境没有先天知识的软内窥镜。提议一个基于学习的适应性视觉透视控制器,在线更新眼部-手动视觉机动配置,并指导内视镜,该内镜通过视觉变异器(VIT)以单视深度估计为指导。为了防止与周围的解剖情况发生不必要和过度的互动,通过整个内镜3-D式透视仪,从嵌入的纤维布拉格格传感器中引入了一种能源驱动型形状规划算法。此外,还开发了一个模型预测控制(MPC)战略,以最大限度地减少弹性潜在能源流动,同时优化指导政策。为了防止通过视觉变压式变压框架,在数个自动修正的底镜中演示环境中进行弹性调整。</s>