Magnetic Encoder Calibration via Periodic Distortion Removal
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Solution Overview
Problem
Existing sensor calibration methods for robots are impractical and fail to account for distortions introduced by robotic system mounting geometries and mechanical variations, leading to inaccurate position measurement data.
Innovation Solution
A method involving data processing hardware that receives measurement data from a position measurement system with a nius track and a master track, identifies periodic distortion, decomposes it into periodic components, and removes these components from the measurement data to generate a calibration profile for calibrating the position measurement system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing sensor calibration methods are used, then the calibration process is simple, but the measurement precision deteriorates due to unaccounted distortions from mounting geometries and mechanical variations
Solution Approach 1:
The calibration method performs preliminary identification and removal of periodic distortion components from measurement data before final position calculation. By pre-processing the measurement data to eliminate systematic errors caused by mounting geometries and mechanical variations, the system achieves high measurement precision without requiring complex hardware modifications or iterative calibration procedures
Solution Approach 2:
The method extracts and removes periodic distortion components from the measurement data obtained from the position measurement system. By separating the useful position information from the harmful periodic distortions through signal processing, the system recovers accurate position measurements while filtering out errors introduced by mechanical variations and mounting geometries
2Measurement precision
If calibration accounts for mounting geometries and mechanical variations, then measurement precision improves, but the calibration method becomes more complex and impractical
Solution Approach 1:
The calibration system uses the position measurement system itself to generate calibration data by moving through known positions. The system self-calibrates by identifying periodic distortions in its own measurement output without requiring external reference instruments or complex setup procedures, making the calibration process both accurate and easy to implement
Solution Approach 2:
The method transforms the calibration problem from adjusting physical parameters of the measurement system to processing measurement data parameters. By changing from hardware-level calibration to software-level signal processing, the system achieves high precision while maintaining operational simplicity through mathematical decomposition and removal of distortion components
Data Source
AI summary
A method for calibrating a position measurement system includes receiving measurement data from the position measurement system and determining that the measurement data includes periodic distortion data. The position measurement system includes a nonius track and a master track. The method also includes modifying the measurement data by decomposing the periodic distortion data into periodic components and removing the periodic components from the measurement data.