Robotic Picking Training Using 3D Models and Center of Gravity
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Solution Overview
Problem
Automated storage and retrieval systems face challenges in efficiently picking packages of varying size, weight, and orientation due to non-uniform weight distribution and random orientations of articles at the picking station.
Innovation Solution
A robotic package retrieval system that determines an optimal picking location by identifying the article, creating a 3D model, and determining the 3D center of gravity, which is then used to select the appropriate picking location, regardless of the article's weight distribution or orientation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a robotic manipulator picks articles with non-uniform weight distribution and random orientations, then the system can handle diverse packages, but the picking accuracy and efficiency deteriorate due to difficulty in determining optimal grasp points
Solution Approach 1:
The system performs preliminary actions by creating comprehensive 3D models and determining center of gravity locations for all articles before the actual picking operation. This advance preparation allows the robotic manipulator to quickly retrieve pre-calculated optimal grasp points from storage, eliminating the need for complex real-time calculations during picking operations.
Solution Approach 2:
The system creates digital 3D models that are copies of the physical articles, including their geometric features and weight distribution characteristics. These digital models allow the system to simulate and determine optimal grasp points without physically manipulating the actual articles, improving both accuracy and efficiency.
2Measurement precision
If the robotic system determines optimal picking locations through real-time 3D modeling and center of gravity calculation, then picking accuracy improves, but the processing time and system complexity increase
Solution Approach 1:
The system performs all complex 3D modeling and center of gravity calculations in advance, before the articles need to be picked. The optimal grasp points are determined and stored beforehand, allowing the robotic manipulator to simply retrieve this pre-calculated information during operation, dramatically reducing processing time while maintaining high accuracy.
Solution Approach 2:
The system replaces real-time mechanical sensing and trial-and-error physical manipulation with pre-computed digital models and gravitational calculations. This substitution of computational methods for physical experimentation eliminates time-consuming iterative adjustments during the actual picking process.
3Ease of operation
If the system creates detailed 3D models and determines center of gravity for each article, then the optimal picking location selection improves, but the computational resources and system complexity increase
Solution Approach 1:
The system performs all complex computational tasks of 3D modeling and center of gravity determination in advance, storing the results for later retrieval. This shifts the computational burden from the operation phase to the preparation phase, making the actual picking operation simpler and more straightforward.
Solution Approach 2:
The system creates digital 3D models as simplified representations of physical articles. These digital copies contain all necessary geometric and gravitational information, allowing the system to work with lightweight data structures rather than complex physical analysis during operation, reducing computational complexity.
Data Source
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AI summary
A method of training a robot to pick a plurality of articles includes determining an identity of an article and determining if the article has a uniform distribution of weight. If the article has a uniform distribution of weight, a three-dimensional (3D) model of the article is created and stored in a database record associated with the identity. If the article does not have a uniform distribution of weight, a 3D model is created, two-dimensional (2D) images of each side of the article are created, and a 3D center of gravity of the article is determined. The 3D model, the 2D images, and the 3D center of gravity of the article are stored in a database record associated with the identity of the article.