Split Robotic Reference Frame for Occlusion-Resilient Navigation
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Solution Overview
Problem
Existing surgical navigation systems face challenges in maintaining clear lines of sight for tracking markers due to obstructions during robotic surgery, leading to difficulties in determining the pose of robotic arms and verifying pose information, which affects navigation accuracy and operation efficiency.
Innovation Solution
A virtual reference frame is created using a base set of tracking markers on a robot base and additional sets on robotic arm segments, allowing for pose determination and verification even when some markers are blocked, by combining sensor data and projected patterns to form a redundant tracking system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single set of tracking markers is used on the robotic arm, then the system is simpler, but the line of sight may be blocked during surgery affecting tracking accuracy
Solution Approach 1:
The tracking marker system is segmented into multiple separate sets distributed at different locations on the robotic arm. This segmentation ensures that if one set is blocked, other sets remain visible to the optical sensor, maintaining continuous and reliable tracking of the robotic arm's position and orientation throughout the surgical procedure.
Solution Approach 2:
Multiple tracking marker sets are positioned at different spatial dimensions along the robotic arm (proximal, intermediate, and distal locations). This three-dimensional distribution creates redundant viewing angles, ensuring that the optical sensor can detect at least one unblocked marker set regardless of the robotic arm's position or orientation during surgery.
2Reliability
If multiple sets of tracking markers are added to ensure continuous tracking, then tracking reliability improves, but the complexity of the system increases
Solution Approach 1:
Multiple tracking marker sets and their corresponding coordinate frames are merged into a single unified virtual reference frame. The processor integrates data from all marker sets to calculate the robotic arm's pose, treating them as a cohesive system that provides redundant information for more reliable pose determination while managing complexity through software integration.
Solution Approach 2:
The system continuously monitors which marker sets are detectable and uses this feedback to adjust the virtual reference frame construction. When certain marker sets are blocked, the system automatically relies on alternative detectable sets, ensuring continuous and reliable pose determination without requiring all markers to be visible at all times.
3Measurement precision
If traditional single-marker tracking is used, then the registration and calibration procedures are simpler, but navigation accuracy is reduced
Solution Approach 1:
The system performs preliminary registration and calibration by establishing the coordinate transformation between the base coordinate frame and each individual marker set coordinate frame before surgery. These pre-computed transformations are stored and used during the procedure, eliminating the need for complex real-time registration while achieving high navigation accuracy through the accumulated precision of multiple marker sets.
Data Source
AI summary
A robotic navigation system includes a robot base; a robotic arm with a proximal end secured to the robot base, a distal end movable relative to the proximal end, and one or more arm segments between the proximal end and the distal end; a base set of tracking markers secured to the robot base; and at least one additional set of tracking markers secured to the robotic arm.