Robotic Endoscope Navigation for Occlusion-Resilient GI Lumen Tracking
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Solution Overview
Problem
Existing endoscope navigation technologies struggle with robustness and adaptability, particularly in navigating the gastrointestinal tract, due to computational constraints and the inability to handle occlusions and varying anatomical structures, limiting their diagnostic and therapeutic capabilities.
Innovation Solution
A robotic endoscope system with a high-level state machine for gross region prediction, turn estimation, and multiple state-dependent controllers for center tracking and wall-avoidance, enabling navigation even under occlusion and anticipating sharp turns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional endoscopes are used, then diagnostic and treatment capabilities are maintained, but navigation robustness and adaptability are insufficient
Solution Approach 1:
The system dynamically adapts its navigation strategy based on real-time image analysis. The controller adjusts navigation commands by analyzing lumen center positions, detecting occlusions, and identifying anatomical features, allowing the endoscope to adapt to varying anatomical structures and navigation challenges throughout the procedure
Solution Approach 2:
The system continuously captures images, analyzes lumen center positions and anatomical features, and uses this feedback to generate real-time navigation commands. This closed-loop feedback mechanism enables robust navigation by constantly adjusting the navigation path based on actual anatomical conditions and detected occlusions
2Measurement precision
If computational algorithms are added for autonomous navigation, then navigation precision is improved, but device complexity increases
Solution Approach 1:
The system uses image processing algorithms as an intermediary between the camera and the navigation controller. These algorithms analyze captured images to identify lumen centers and anatomical features, converting visual information into navigation-relevant data without requiring complex hardware modifications
Solution Approach 2:
The system replaces complex mechanical navigation aids with computational image analysis. Instead of adding mechanical structures for physical guidance, the system uses software-based lumen center detection and tracking algorithms to achieve precise navigation control
3Ease of operation
If autonomous navigation is implemented, then ease of operation is improved, but ability to handle occlusions and varying anatomy is reduced
Solution Approach 1:
The system performs preliminary image analysis to detect potential occlusions and anatomical variations before they become navigation obstacles. By analyzing images in advance and predicting upcoming anatomical features, the system can proactively adjust its navigation path to handle occlusions and varying anatomy effectively
Data Source
AI summary
Implementations include herein are visual navigation strategies and systems for lumen center tracking comprising a high-level state machine for gross (i.e., left/right/center) region prediction and curvature estimation and multiple state-dependent controllers for center tracking, wall-avoidance and curve following. This structure allows a navigation system to navigate even under the presence of significant occlusion that occurs during turn navigation and to robustly recover from mistakes and disturbances that may occur while attempting to track the lumen center. This system comprises a high-level state machine for gross region prediction, a turn estimator for anticipating sharp turns, and several lower level controllers for heading adjustment.