Interventional Device Training Data for Accurate Pose Control
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Solution Overview
Problem
Existing positioning control techniques for interventional devices, particularly in clinical settings, face challenges due to the deformable nature of soft-tissue human organs and environmental conditions, leading to inaccurate and unpredictable robotic manipulations.
Innovation Solution
A predictive model-based feed-forward and feedback positioning control system for interventional devices, utilizing continuous positioning controllers and data collection methods to enhance navigated pose estimation and motion control, incorporating sensors for real-time adjustments and machine learning models to adapt to anatomical variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mathematical modeling is used to control continuum robot configuration, then positioning control is achieved, but accuracy is susceptible to environmental changes and manufacturing inaccuracies
Solution Approach 1:
The system performs preliminary data collection during a calibration phase where the continuum robot is positioned at multiple known poses. This training data is stored and used later to train machine learning models that predict pose from actuator commands, preparing the system in advance to compensate for environmental and manufacturing variations.
Solution Approach 2:
The system implements feedback by comparing predicted poses with actual measured poses during calibration. The machine learning model is trained using this feedback to minimize prediction errors, and the calibrated model continuously compensates for deviations caused by environmental changes and manufacturing inaccuracies during operation.
2Adaptability or versatility
If degree of freedom is increased to improve maneuverability, then robot can navigate complex anatomical structures, but kinematics complexity increases
Solution Approach 1:
The system introduces machine learning models as an intermediary between actuator commands and end-effector pose prediction. Instead of directly computing complex kinematics equations for high-DOF continuum robots, the ML model learns the mapping from actuator states to poses, simplifying the control computation while maintaining the ability to handle complex maneuvers.
Solution Approach 2:
The system replaces traditional mechanical kinematics computation with data-driven machine learning models. The complex analytical kinematics equations are substituted with trained neural networks that predict pose from actuator commands, reducing computational complexity while preserving the robot's high maneuverability capabilities.
3Stability of the object's composition
If continuous control is implemented for deformable structures, then smooth motion is achieved, but control difficulty increases due to modeling complexity
Solution Approach 1:
The system implements self-service by using the continuum robot's own operational data to train the machine learning model. During calibration, the robot is manually positioned at various poses and the corresponding actuator commands and measured poses are recorded. This self-collected data trains the model to predict poses accurately, enabling smooth continuous control without requiring complex external modeling.
Data Source
AI summary
A training data collection method for an interventional device (130) including a portion of an interventional device (140) and sensors (332) adapted to provide position and/or orientation and/or shape information with at least a part of the sensors being affixed to said portion of device (140). The method involves controlling one or more motion variables of the interventional device (130) in accordance with a pre-defined data point pattern, and determining, from shape data derived from said information, an estimating of a pose of said device portion (140) and an estimating of a positioning motion of the interventional device (130). The method further involves a storage of a temporal data sequence for the interventional device (130) derived from the estimated pose of the end-effector (140) for each data point, the estimated positioning motion of the interventional device (130) for each data point, and the motion variable(s) of the interventional device (130) for each data point.