Robotic Edge Milling Pose Calibration via 3D Error Tracking
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Solution Overview
Problem
Current robotic skin edge milling methods face challenges with large calibration errors and low accuracy due to limited data stability, inability to account for cutter rotation deviations and deformations, and reliance on manual feature point identification.
Innovation Solution
A workpiece and cutter pose calibration method based on robotic edge milling error tracking, which involves constructing an edge milling path, generating and matching point clouds, calculating errors, and updating pose parameters to achieve precise calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration methods (ejector, LVDT, ball, laser tracker) are used to determine workpiece and cutter pose, then calibration can be completed with existing methods, but calibration accuracy is low and calibration errors are large
Solution Approach 1:
The patent implements a closed-loop feedback calibration system where the robot performs edge milling operations, the actual milling trajectory is measured and compared with the planned trajectory, and the pose parameters are iteratively adjusted based on the detected errors. This feedback mechanism continuously refines the calibration accuracy by using the actual operation results to correct the pose parameters, transforming the open-loop traditional calibration into a closed-loop adaptive system that improves both measurement precision and data stability.
Solution Approach 2:
The patent replaces traditional mechanical calibration methods (ejector contact, LVDT measurement, ball probe contact) with a computational approach based on trajectory point cloud matching. Instead of relying on mechanical contact and manual feature point identification, the system uses robotic operation trajectory data, three-dimensional point cloud acquisition, and mathematical optimization algorithms to determine pose parameters, thereby substituting mechanical measurement systems with intelligent computational systems that achieve higher accuracy and stability.
2Measurement precision
If manual feature point identification is used in calibration, then calibration can be performed with simple equipment, but calibration accuracy is limited and consistency is poor
Solution Approach 1:
The patent replaces manual feature point identification with an automated three-dimensional point cloud acquisition and matching system. The robot equips sensors to automatically capture the workpiece geometry and milling trajectory, and computational algorithms automatically identify corresponding points and calculate pose parameters through point cloud registration, eliminating the need for manual observation and identification while significantly improving accuracy and consistency.
Solution Approach 2:
The patent creates a digital copy of the workpiece geometry and milling trajectory through three-dimensional point cloud acquisition. Instead of manually identifying physical feature points on the actual workpiece, the system captures the complete geometric information as a digital point cloud model, then uses this digital copy for automated feature point matching and pose calculation, thereby improving measurement precision while reducing device complexity.
3Reliability
If traditional calibration methods are used, then calibration can be completed without error tracking, but the system cannot compensate for cutter rotation deviations and deformations
Solution Approach 1:
The patent implements comprehensive error tracking and compensation by continuously monitoring the actual milling trajectory during robot operations. The system detects deviations between planned and actual trajectories, identifies sources of error including cutter rotation deviations and deformations, and feeds this information back to adjust pose parameters and compensate for errors, thereby improving reliability and enabling application to complex aerospace components with thin-walled curved surfaces.
Solution Approach 2:
The patent transforms the static traditional calibration approach into a dynamic adaptive system that continuously tracks and compensates for errors during actual operation. The pose parameters are not fixed after initial calibration but are dynamically adjusted based on real-time trajectory monitoring and error analysis, allowing the system to adapt to cutter rotation deviations, deformations, and other dynamic factors that affect milling accuracy on complex components.
Data Source
AI summary
A workpiece and cutter pose calibration method based on robotic edge milling error tracking, including: 1. generating an edge milling trajectory point cloud; 2. obtaining an actual edge milling three-dimensional point cloud; 3. generating an updated edge milling three-dimensional point cloud; 4. calculating an edge milling allowance error and a posture inclination error; 5. solving position errors of the workpiece and cutter; 6. solving posture errors of the workpiece and cutter; 7. updating pose parameters of the workpiece and cutter; 8. repeating steps 4 to 7 until pose error vectors of the workpiece and cutter are both not greater than corresponding preset thresholds. The disclosure performs error comparison, error modeling, and error tracking on the three-dimensional point cloud and edge milling trajectory point cloud, even if the cutter has system errors such as axis deviation, the disclosure can accurately identify pose errors of the workpiece and cutter during edge milling.