Surgical Robot Instrument Drive Anomaly Detection Using ML
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Solution Overview
Problem
Anomalies in surgical instruments used in robotic systems, such as cable wear or interference, can lead to unexpected forces and impede tool tip motion, affecting surgical precision and requiring early detection or replacement.
Innovation Solution
A machine-learned model is employed to predict normal operation based on sensor measurements, comparing predicted and actual operation to detect anomalies in the instrument drive chain, enabling early detection and corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensor-based monitoring is used to detect cable wear and interference, then the system can identify anomalies, but the detection precision is insufficient to account for unmodeled effects like friction and hysteresis
Solution Approach 1:
The patent replaces traditional mechanical sensor-based monitoring with a machine-learned model that processes sensor data to predict cable forces. This substitution enables the system to account for unmodeled effects like friction and hysteresis by learning from historical data, significantly improving anomaly detection precision without requiring additional physical sensors or complex mechanical monitoring systems.
Solution Approach 2:
The machine-learned model acts as an intermediary between raw sensor measurements and anomaly detection. It processes sensor data through trained algorithms that incorporate knowledge of friction, hysteresis, and other unmodeled effects, transforming simple sensor readings into precise anomaly detection capabilities without directly increasing hardware complexity.
2Measurement precision
If machine-learned models are used to predict normal operation and detect anomalies, then detection precision improves, but computational requirements and processing time increase
Solution Approach 1:
The machine-learned model is trained offline in advance using historical sensor data and labeled anomaly information. This preliminary training phase allows the model to learn complex patterns and relationships during normal operation, so that during actual surgical procedures, the model can rapidly compare predicted vs. actual cable forces and detect anomalies in real-time without requiring extensive computational resources during the critical surgical workflow.
Data Source
AI summary
A machine-learned model is used in the detection of anomalies in operation of the instrument drive chain of a surgical robotic system. For example, normal operation (e.g., cable force) based on operation information (e.g., position and/or load) is predicted by the machine-learned model, which prediction is then compared to actual operation. The comparison results in early detection of anomalies based on machine-learned prediction and the corresponding incorporation of historical operation.