Robotic Surgery UI Tracking Correction With LQE Up-Sampling
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Solution Overview
Problem
Robotic surgery systems face challenges with noisy or imprecise tracking input data from sensor circuits, which can lead to imprecise movements of surgical tools, and often have sampling rates that do not match the desired rate of the control system, resulting in latency and reduced precision.
Innovation Solution
A linear quadratic estimator (LQE) is employed to correct and up-sample the tracking input data, integrating latency reduction and up-sampling functions within its prediction phase, using a Kalman filter gain to compute updated output samples and reduce latency by tuning a latency coefficient, and calculating velocity terms by fitting lines to previous output samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the sensor circuit sampling rate is increased to match the controller sample rate, then the tracking input precision is improved, but the system latency and complexity increase
Solution Approach 1:
The LQE performs prediction calculations in advance during the prediction phase, estimating the current state before the update phase receives new measurements. This preliminary action allows the system to provide timely output samples without waiting for the next sensor reading, thereby reducing latency while maintaining precision through predictive estimation.
Solution Approach 2:
The LQE acts as an intermediary between the sensor circuit and the controller, processing noisy tracking input data through its prediction and update phases. It transforms the raw sensor data into corrected, up-sampled output samples that match the controller's sampling rate, effectively mediating the data rate mismatch while reducing latency through its computational framework.
2Productivity
If the LQE prediction phase repeats at a higher rate to up-sample the tracking input, then the output sample rate matches the controller rate, but the computational complexity increases
Solution Approach 1:
The LQE dynamically switches between its prediction phase and update phase based on data availability. The prediction phase repeats at a higher rate to generate multiple output samples, while the update phase is triggered only when new tracking input samples are received. This dynamic operation allows the system to achieve high output sample rates without continuously performing full computational cycles, thereby managing computational complexity effectively.
3Loss of time
If the LQE uses a tuned latency coefficient in the prediction phase, then the output latency is reduced, but the tuning complexity increases
Solution Approach 1:
The LQE uses a latency coefficient as a tunable parameter in the prediction phase to control the timing and weighting of predictions. By adjusting this parameter, the system can optimize output latency to match specific surgical robotic applications. The coefficient modifies how past states are weighted in the prediction, allowing fine-tuned control over latency characteristics.
Data Source
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.