Image-Based Stacking Alignment Control for Material Handling
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Solution Overview
Problem
Material handling equipment faces challenges in accurately aligning stacked objects, which affects operation safety due to factors like inaccurate pickup poses, uneven ground, and cumulative errors, leading to misalignment during stacking.
Innovation Solution
A method involving a controller that uses sensors to acquire target images of stacking objects, determines pixel differences between these images, and compares them with a threshold to assess alignment, ensuring precise alignment through controlled pose adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If material handling equipment stacks objects based on basic positioning, then stacking speed is maintained, but alignment accuracy deteriorates due to inaccurate pickup poses, uneven ground, and cumulative errors
Solution Approach 1:
The system performs preliminary image acquisition and alignment detection before the actual stacking operation. The controller captures images of both the first and second stacking objects, processes these images to determine their respective positions and orientations, and calculates alignment status in advance. This preliminary action allows the system to identify misalignment issues before they affect the stacking operation, enabling corrective measures to be taken without compromising the overall stacking speed.
Solution Approach 2:
The system implements a feedback mechanism where the controller continuously monitors the alignment status by processing images of the stacking objects. Based on the detected alignment deviations, the controller can adjust the positioning of the material handling equipment or the stacking objects themselves. This closed-loop feedback ensures that alignment accuracy is maintained while allowing the system to adapt to variations in pickup poses, ground unevenness, and cumulative positioning errors.
2Manufacturing precision
If image processing is performed to detect alignment, then alignment accuracy is improved, but processing time increases
Solution Approach 1:
The image processing task is segmented into distinct functional modules: image acquisition from multiple sources, feature extraction to identify key positioning elements, alignment calculation based on extracted features, and control signal generation. This segmentation allows each module to be optimized independently and enables parallel processing where possible, reducing overall processing time while maintaining detection accuracy.
Solution Approach 2:
The system processes only the critical features and regions necessary for alignment detection rather than analyzing entire images in full detail. By focusing computational resources on the most relevant portions of the images (such as the interfaces between stacking objects and the ground), the system achieves sufficient alignment detection accuracy with reduced processing time and computational load.
Data Source
AI summary
A method for determining an alignment state includes: acquiring, by using a sensor, target images of a first stacking object and a second stacking object; acquiring, from the target images, a first target image region of the first stacking object and a second target image region of the second stacking object; and determining a pixel difference between the first target image region and the second target image region, and comparing the pixel difference with a threshold, to determine an alignment state between the first stacking object and the second stacking object. Embodiments of the present disclosure are used to implement alignment between a first stacking object and a second stacking object during stacking.