Machine Vision Misalignment Detection and Compensation
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Solution Overview
Problem
Machine vision systems face accuracy and performance degradation due to mechanical misalignments caused by aging, thermal expansion, vibration, and environmental factors, which affect the ability to detect objects and maintain safety-critical operations.
Innovation Solution
A method and apparatus that detect mechanical misalignments in real-time and compensate image processing by estimating misalignments based on reference marker positions, calculating a worst-case error, and transitioning to a fault state if the error exceeds a defined threshold, ensuring the system's safety and operational integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the machine vision system operates continuously for extended periods, then productivity is improved, but mechanical misalignments accumulate due to aging, thermal expansion, and vibration, degrading measurement precision
Solution Approach 1:
The system performs preliminary calibration during setup to establish reference marker positions, and then uses these pre-established references to continuously detect and compensate for misalignments during operation, allowing long-term productivity without degradation
Solution Approach 2:
The system continuously monitors reference marker positions during operation, compares them against original calibration data, and uses this feedback to detect misalignments and trigger recalibration when thresholds are exceeded, maintaining precision over extended periods
2Measurement precision
If the system performs continuous monitoring and compensation for misalignments, then measurement precision is maintained, but device complexity increases due to additional processing steps
Solution Approach 1:
The system extracts only the essential alignment information by monitoring reference marker positions rather than analyzing entire images, and separates the calibration function into a preliminary setup phase, reducing ongoing processing complexity
Solution Approach 2:
The system changes from full image processing to monitoring specific parameter changes in reference marker positions, which simplifies the processing while maintaining the ability to detect misalignments
3Reliability
If the system transitions to fault state on detecting excessive misalignment, then reliability is improved by preventing inaccurate measurements, but productivity is reduced due to operational disruptions
Solution Approach 1:
The system applies partial compensation for misalignments within acceptable thresholds, allowing operation to continue with reduced but still acceptable precision, and only transitions to fault state when misalignments exceed critical limits, thus maintaining productivity while ensuring reliability
Data Source
AI summary
According to one aspect of the teachings herein, a method and apparatus detect mechanical misalignments in a machine vision system during run-time operation of the machine vision system, and compensate image processing based on the detected misalignments, unless the detected misalignments are excessive. Excessive misalignments may be detected by determining a worst-case error based on them. If the worst-case error exceeds a defined limit, the machine vision system transitions to a fault state. The fault state may include disrupting operation of a hazardous machine or performing one or more other fault-state operations. Among the detected misalignments are internal misalignments within individual cameras used for imaging, and relative misalignments between cameras. The method and apparatus may further perform run-time verifications of focus and transition the machine vision system to a fault state responsive to detecting insufficient focal quality.