Curved Article Positioning via Point Cloud Normal Vectors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In the food processing industry, particularly in the handling of curved articles like poultry carcasses, manual assessment and saddling of these articles are labor-intensive, prone to human error, and pose a risk of injury, hindering fully automated processing.
Innovation Solution
A method using a detection device to generate point cloud data, which is then processed to calculate a normal vector describing the spatial orientation of the article. This normal vector is used to determine the precise position and orientation of the article, enabling automatic alignment and saddling on a support body.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual assessment and saddling of curved articles is used, then flexibility and adaptability are maintained, but productivity is reduced and human injury risk increases
Solution Approach 1:
The patent replaces the manual mechanical assessment system with an optical detection device that generates point cloud data. The detection device captures the spatial surface structure of curved articles, and a processing unit automatically determines position and orientation through coordinate system transformations, eliminating the need for manual visual assessment and reducing human injury risk while increasing productivity
Solution Approach 2:
The patent introduces an intermediary computational process that transforms point cloud data into a reference coordinate system and calculates position and orientation parameters. This intermediary processing layer between detection and execution enables automated decision-making for saddling operations, allowing high-speed processing without direct human involvement
2Productivity
If manual saddling by employees is used, then complex spatial assessment can be performed, but labor costs and processing time increase
Solution Approach 1:
The patent implements continuous automated processing where the detection device continuously captures point cloud data, the processing unit continuously transforms coordinates and calculates parameters, and the saddling mechanism continuously positions articles. This continuous automated action eliminates idle time between manual assessments and significantly increases processing speed while reducing time loss per article
3Productivity
If automated processing is implemented without precise position determination, then productivity increases, but manufacturing precision decreases
Solution Approach 1:
The patent replaces imprecise manual visual assessment with an optical detection device that generates detailed point cloud data representing the spatial surface structure. The processing unit performs precise coordinate transformations to determine position and orientation, achieving high manufacturing precision that enables full automation without sacrificing accuracy
Solution Approach 2:
The patent transitions from two-dimensional image data to three-dimensional point cloud data, adding depth information through coordinate system transformations. This dimensional enhancement provides comprehensive spatial information about curved articles, enabling precise determination of position and orientation in three-dimensional space while maintaining high automation levels
Data Source
AI summary
A method, apparatus and arrangement for determining food processing curved articles position and automatically saddling such articles includes: a) scanning the article by a detection device to generate point cloud data representing the article spatial surface structure; b) selecting a central point, determining a partial point cloud from the points within a predefined distance around the central point; c) calculating the partial point cloud principal orientation for two spatial directions by determining the two associated spatial position vectors; d) calculating a partial surface normal vector based on the determined spatial position vectors that is perpendicular to both vectors; e) marking partial point cloud points as processed, and repeating steps b-d for further unmarked points until a predefined proportion are processed; f) selecting a predefined number of calculated partial surface normal vectors and calculating overall orientation of the article by determining a normal vector based on selected partial surface normal vectors.


