Surgical Robot Error Detection with Split Tracking Data Paths
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Solution Overview
Problem
Surgical robotic systems face delays in detecting errors or loss of accuracy due to high-speed tracking data noise and control system stability issues, leading to potential damage during surgical procedures.
Innovation Solution
A surgical system comprising a manipulator, a navigation system with a tracker and localizer, and controllers that determine and combine transforms to detect errors in real-time, using data fusion and filtering techniques to ensure accurate positioning and prevent system instability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If low-pass filtering is applied to tracking data to reduce noise, then signal-to-noise ratio improves and robot movement becomes smoother, but error detection is delayed due to filtering processing time
Solution Approach 1:
The patent divides the tracking data processing into separate streams: one stream applies low-pass filtering for smooth robot control, while another stream processes raw or minimally filtered data for real-time error detection. This segmentation allows both noise reduction and timely error detection to occur simultaneously without compromising either function.
Solution Approach 2:
The patent introduces an intermediary error detection mechanism that operates between the tracking system and the robot control system. This intermediary layer monitors transformed values from both filtered and unfiltered data streams, comparing them to detect errors before they affect robot operation, thus bridging the gap between noise reduction and real-time monitoring.
2Measurement precision
If tracking system operates at high speed to capture robot movement, then positioning accuracy improves, but control system stability deteriorates due to noise and excessive bandwidth
Solution Approach 1:
The patent applies different quality levels of data processing to different control functions: high-speed raw tracking data is used for error detection where speed is critical, while low-pass filtered data is used for robot control where smoothness and stability are prioritized. This local differentiation of data quality resolves the contradiction between positioning accuracy and control stability.
Solution Approach 2:
The patent uses partial filtering rather than complete filtering of tracking data. By applying low-pass filtering only to the control commands while maintaining access to unfiltered data for error detection, the system achieves sufficient noise reduction for stable control without completely eliminating high-frequency information needed for accurate positioning monitoring.
3Speed
If outer positioning loop bandwidth is increased to improve response speed, then robot responsiveness improves, but system stability deteriorates
Solution Approach 1:
The patent performs preliminary error detection on tracking data before the filtered data is used for control. By checking transformed values from raw tracking data against expected ranges in advance, the system can identify and flag potential errors before they propagate through the control loop, enabling faster response to errors without compromising overall system stability.
Data Source
AI summary
Systems and methods for detecting an error in a surgical system. The surgical system includes a manipulator with a base and a plurality of links and the manipulator supports a surgical tool. The system includes a navigation system with a tracker and a localizer to monitor a state of the tracker. Controller(s) determine values of a first transform between a state of the base of the manipulator and a state of one or both of the localizer and the tracker of the navigation system. The controller(s) determine values of a second transform between the state of the localizer and the state of the tracker. The controller(s) combine values of the first transform and the second transform to determine whether an error has occurred relating to one or both of the manipulator and the localizer.