  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.
