Robotic Surgery UID Tracking Correction With LQE Up-Sampling
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Solution Overview
Problem
Robotic surgery user interface device (UID) tracking data is often noisy and imprecise, requiring correction to ensure precision and repeatability in end effector movements, and its sampling rate may differ from the desired rate of the robotic surgery controller, necessitating a solution that corrects and up-samples the data without introducing high latency.
Innovation Solution
A linear quadratic estimator (LQE) is employed, which integrates latency reduction and up-sampling functions into its prediction phase, using a Kalman filter gain to compute updated output samples based on previous estimates and new tracking input, and tunes a latency coefficient to minimize latency while avoiding overshoot.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a sensor subsystem tracks UID orientation and position, then real-time tracking data is produced, but the tracking data becomes noisy or imprecise
Solution Approach 1:
A linear quadratic estimator (LQE) is introduced as an intermediary processing layer between the sensor subsystem and the robotic surgery controller. The LQE receives noisy tracking data from sensors (electromagnetic tracker, optical tracker, or inertial sensors) and produces corrected, filtered output data that maintains temporal relationships while eliminating noise and imprecision.
2Productivity
If the sensor circuit sampling rate is slower than the controller sample rate, then the sensor circuit can operate at lower power, but the controller cannot process data at the desired higher rate
Solution Approach 1:
The LQE performs preliminary computational action by pre-processing and filtering tracking data at the sensor circuit's native sampling rate before presenting data to the controller. This preliminary filtering eliminates the need for the sensor circuit to operate at higher rates, reducing power consumption while still providing the controller with data at the desired higher effective rate through intelligent interpolation and prediction.
3Measurement precision
If tracking data is corrected through filtering, then noise is reduced, but latency is introduced
Solution Approach 1:
The LQE implements dynamic adaptive filtering that adjusts its processing characteristics based on the specific application requirements. By using a linear quadratic estimator rather than fixed-order filtering, the system can dynamically balance between noise reduction and latency minimization, optimizing the trade-off between tracking data quality and real-time responsiveness for different surgical scenarios.
Data Source
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AI summary
A sequence of tracking input samples that are measures of position or orientation of a user interface device, UID, being held by a user, are received. In a prediction phase, a current output sample of a state of linear quadratic estimator, LQE, is computed that is an estimate of the position or orientation of the UID. The current output sample is computed based on i) a previously computed output sample, and ii) a velocity term. In an update phase, an updated output sample of the state of the LQE is computed, based on i) a previously computed output sample from the prediction phase, and ii) a most recent tracking input sample. Other embodiments are also described and claimed.