Forward-Kinematics ML Control for End Effector Position Accuracy
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Solution Overview
Problem
Existing techniques for controlling the actuators of computer-controlled processing machines, such as robots and 3D printers, struggle with precise positioning and orientation of end effectors due to factors like thermal expansion and vibration, often requiring additional hardware and sensors, which can increase complexity and cost.
Innovation Solution
A forward-kinematics machine-learning (ML) model is developed to predict and correct the positioning of end effectors with high accuracy by learning from measured and predicted locations, reducing the need for closed-loop control systems and additional sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If structural design features are implemented to increase passive resistance (mass, rigidity, damping), then positioning precision is improved, but device complexity and manufacturing difficulty increase
Solution Approach 1:
The patent replaces mechanical solutions (increasing mass, rigidity, damping features) with a computational approach using machine learning models. The system uses sensors to collect data about actual positions, compares them with expected positions from kinematic models, and uses the differences to train ML models that predict correction values, substituting physical reinforcement with intelligent control algorithms
Solution Approach 2:
The patent changes the parameter space by introducing environmental parameters (temperature, humidity, pressure) and operational parameters (axis positions, velocities) as inputs to the machine learning model. This allows the system to adapt to varying conditions without changing the physical structure, maintaining precision through parameter-based compensation rather than structural reinforcement
2Manufacturing precision
If inverse kinematics models with calibrated position samples are implemented, then positioning precision is improved, but additional calibration procedures and specialized hardware are required
Solution Approach 1:
The system performs self-calibration by using its own operational data. The ML model is trained using position data collected during normal operation, comparing actual sensor readings with expected positions from the inverse kinematics model. This eliminates the need for external calibration procedures and specialized calibration hardware, as the system calibrates itself using its existing components and operational experience
Solution Approach 2:
The patent performs preliminary training of the ML model using a dataset collected during initial operation or setup. This training phase prepares the model in advance to compensate for positioning errors, so that once trained, the system automatically applies corrections during normal operation without requiring repeated calibration procedures
3Manufacturing precision
If closed-loop control systems with external reference (vision systems, touch sensors) are implemented, then trajectory accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The patent creates a virtual model (machine learning model) that copies and simulates the complex relationships between actuator positions and end effector location. Instead of using physical vision systems or touch sensors to measure and correct position, the system uses a computational copy of the kinematic behavior that predicts and compensates for errors, replacing expensive sensing hardware with intelligent software
4Manufacturing precision
If additional sensors and controls are added for real-time position correction, then positioning precision is improved, but the speed of the process is limited
Solution Approach 1:
The ML model is trained in advance on a comprehensive dataset covering the full operating range of the system. This preliminary training allows the model to make rapid predictions during operation without needing to collect and process additional sensor data in real-time, maintaining high process speed while achieving high precision through pre-computed correction strategies
Data Source
AI summary
A computer-implemented method for generating a forward-kinematics machine-learning (ML) model for controlling a machine includes: based on a target state for the machine, determining an ideal roto-translation for an end effector associated with the machine; using a first version of the forward-kinematics ML model, determining a predicted roto-translation for the end effector based on the target state; determining a predicted location for at least one calibration point on the end effector based on the predicted roto-translation for the end effector; determining a difference between the predicted location for the at least one calibration point and a measured location for the at least one calibration point; and generating a second version of the forward-kinematics ML model based on the difference.