Robotic Instrument Position Fusion for Hysteresis and Buckling
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Solution Overview
Problem
Existing medical procedures face challenges in accurately determining the location of robotically-enabled instruments within a patient's body, particularly due to discrepancies between robotic data and position sensor data, which can lead to conditions like hysteresis and buckling.
Innovation Solution
A system that compares robotic data with position sensor data to determine a weighting factor for location estimation, adjusting the weighting based on motion estimate disparities to improve accuracy and account for conditions such as hysteresis and buckling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If robotic data is used for location estimation, then the system can track instrument position, but discrepancies and inaccuracies occur due to hysteresis and buckling conditions
Solution Approach 1:
The patent combines multiple data sources (robotic data and position sensor data) into a unified location estimation system. The processor fuses these data streams using weighting factors to produce a more reliable and accurate position estimate than either source could provide alone, directly addressing the inconsistency and inaccuracy problems.
Solution Approach 2:
The system continuously compares robotic data with position sensor data and uses the discrepancies to dynamically adjust weighting factors. This feedback mechanism allows the system to adapt to varying data quality conditions, reducing the impact of hysteresis and buckling errors on location estimation accuracy.
2Measurement precision
If position sensor data is used to correct robotic data, then location accuracy improves, but system complexity increases due to data fusion requirements
Solution Approach 1:
The patent changes the parameters of the data fusion process by dynamically adjusting weighting factors based on data quality assessments. This allows the system to optimize the balance between robotic data and sensor data, improving accuracy while managing processing complexity through adaptive parameter tuning rather than complex algorithms.
3Measurement precision
If multiple data sources are fused, then location accuracy improves, but processing time and computational load increase
Solution Approach 1:
The system applies partial data fusion by using weighting factors that can emphasize one data source over another based on current conditions. This selective approach allows the system to achieve sufficient accuracy without always processing both data sources at full capacity, reducing computational overhead and processing time when full fusion is not necessary.
Data Source
AI summary
A robotic system includes an instrument including an elongate shaft, a robotic manipulator configured to manipulate the elongate shaft of the instrument, and control circuitry communicatively coupled to the robotic manipulator and configured to determine a first estimated position of at least a portion of the elongate shaft of the instrument based at least in part on robotic command data, determine a second estimated position of the at least a portion of the elongate shaft of the instrument based at least in part on position sensor data, compare the first estimated position and the second estimated position, and generate a third estimated position based at least in part on the comparison of the first estimated position to the second estimated position.