Image-Enhanced Autonomous Navigation in Intestinal Robotics: A Threshold-Triggered Approach Combining Dark-Area Localization and Morphological Contour GuidanceShort title: Enhancing Visibility and Navigation Precision in Confined Spaces.

Abstract

Robotic endoscopy offers a potential route to autonomous gastrointestinal navigation. However, autonomous intestinal navigation is hindered by non-rigid lumen deformation and visual degradation from specular reflections, which compromise accurate perception and stable control. We propose a threshold-triggered, vision-driven framework incorporating Navier-Stokes-based highlight suppression. Algorithmically, the system is governed by a threshold-triggered mechanism that acts as a conditional perception gate, dynamically alternating between a lightweight "dark-area guidance" algorithm for approximately centered navigation and a more detailed "contour-based guidance" pipeline for corrective steering. The switching policy avoids executing highlight suppression and morphological contour processing in every frame. In phantom-based trials, the system achieved an average speed of 3.10 cm/s on the triangular path. Highlight suppression increased the proportion of frames with normalized center error below 0.1 from 65% to 71%, below 0.2 from 95% to 97%, and below 0.3 from 98% to 100%. The adaptive mechanism decreased runtime by approximately 2% compared to continuous contour processing. Although this absolute reduction was modest, it recovered approximately 89% of the runtime difference between the contour-only and dark-area-only baselines. These phantom-based results support conditional perception allocation while keeping average processing time close to that of lightweight navigation.