Multi-link Device Parameter Calibration via Segmented Kinematic Modeling
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Solution Overview
Problem
Multi-link devices face precision and accuracy issues due to geometrical, machining, and assembly errors, necessitating a method to minimize errors between actual and ideal values.
Innovation Solution
A parameter calibration method for multi-link devices involves establishing a kinematic model function, acquiring measurement points on parallel planes, recording initial parameter sets, establishing target functions, updating parameter sets, and obtaining parameter modifications to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If parameter calibration is performed using traditional methods, then some level of accuracy improvement is achieved, but the calibration process is time-consuming and complex
Solution Approach 1:
The calibration process is segmented into two distinct phases: coarse calibration using a coarse calibration object (simpler geometry, fewer measurement points) to achieve initial parameter correction, and fine calibration using a fine calibration object (complex geometry, more measurement points) to achieve precise parameter refinement. This segmentation allows the system to quickly eliminate large errors first, then gradually refine accuracy, significantly reducing total calibration time while maintaining high precision.
Solution Approach 2:
The coarse calibration is performed as a preliminary action before fine calibration. By using the coarse calibration object with simpler geometry and fewer measurement points, the system performs initial parameter correction that brings the multi-link device close to the ideal state. This preliminary action reduces the magnitude of errors that need to be corrected in the subsequent fine calibration stage, making the overall process more efficient.
2Measurement precision
If complex calibration procedures are used to improve accuracy, then parameter precision improves, but the calibration complexity increases
Solution Approach 1:
The calibration system is segmented into two independent calibration modules: coarse calibration module and fine calibration module. Each module has its own calibration object and parameter correction focus. The coarse calibration module handles large errors using simpler procedures, while the fine calibration module handles residual errors with more precise procedures. This segmentation reduces overall complexity by breaking down the complex calibration task into manageable stages with different complexity levels.
Solution Approach 2:
The calibration process dynamically adapts its complexity based on the current error magnitude. When large errors are present (after assembly or significant adjustments), the system uses the coarse calibration procedure with simpler measurements and corrections. When errors are small (after coarse calibration), the system transitions to fine calibration with more precise measurements. This dynamic adaptation allows the system to use minimal complexity only when necessary, reducing overall calibration complexity while maintaining accuracy.
Data Source
AI summary
A parameter calibration method includes: establishing a kinematic model function of the multi-link device, the kinematic model function comprising a parameter set; controlling the multi-link device to acquire N measurement points located on a first and a second planes of an object with a measurement tool, and recording N parameter sets as N initial parameter sets when the multi-link device acquires the N measurement points with the measurement tool, wherein the first and the second planes are parallel to each other and have a predetermined dimension; establishing a plurality of target functions associated with the N measurement points based on the N parameter sets and the kinematic model function; updating the N parameter sets based on the target functions to obtain N updated parameter sets; obtaining a parameter modification of the parameter set based on the N initial parameter sets and the N updated parameter sets.