Basics
This section introduces the basic information about the single-case carton depalletizing solution, including the application scope, unsupported functions, and technical specifications.
Application Scope
This section introduces the application scope of the single-case carton depalletizing solution from the aspects of carton types, project requirements, carton surface features, etc.
Carton Types
The table below illustrates the solution’s applicability to carton types.
| In Scope | Illustration | Out of Scope | Illustration |
|---|---|---|---|
Regular cartons with a standard rectangular upper surface |
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Flattened irregularly rectangular cardboards, waste cartons, etc. |
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Project Requirements
The table below illustrates the solution’s applicability to project requirements.
| In Scope | Illustration | Out of Scope | Illustration |
|---|---|---|---|
Single-case carton depalletizing |
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Mixed-case carton depalletizing |
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Single-case carton palletizing |
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Mixed-case carton palletizing |
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Carton Surface Features
The table below illustrates the solution’s applicability to carton surface features.
| In Scope | Illustration | Out of Scope | Illustration |
|---|---|---|---|
Carton edges are clear. The carton’s surface color can be monochromatic or plain, and the surface can have patterns, texts, strapping, or tapes. |
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It is challenging to clearly distinguish whether there is only one carton or there are multiple cartons. For example, the cases when the carton is divided by distinct patterns or stripes, or when the cartons are closely placed together. |
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Carton Placement
The table below illustrates the solution’s applicability to carton placement.
| In Scope | Illustration | Out of Scope | Illustration |
|---|---|---|---|
Individual cartons are of moderate size, namely that each carton occupies an appropriate proportion in the camera’s field of view. |
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The size of individual cartons is too large, occupying the entire field of view of the camera, which may impact the effectiveness of recognition based on deep learning. |
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The camera is horizontally placed within the camera’s field of view, and the angle between the carton and the camera ranges from 0° to 45°. |
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The camera is inclined within the camera’s field of view, and the angle between the carton and the camera is greater than 45°. This may lead to wrong deep learning–based recognition results. |
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Workobject Carriers
The table below illustrates the solution’s applicability to object carriers.
| In Scope | Illustration | Out of Scope | Illustration |
|---|---|---|---|
Cartons are placed on a pallet. |
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Cartons are placed in four-wall bins, containers, or turnover boxes, or there are other obstructions around the carton pallet. |
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Vacuum Gripper Design
The table below illustrates the solution’s applicability to vacuum gripper design.
| In Scope | Illustration | Out of Scope | Illustration |
|---|---|---|---|
Single-section vacuum gripper |
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Multi-section vacuum gripper for picking objects in multiple rows |
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Multi-section vacuum gripper for picking objects in a row |
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Unsupported Functions
The single-case carton depalletizing solution does not support the following functions:
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Identification of the carton’s orientation. When the difference between the length and width of the carton is less than 20 mm, this solution does not support the identification of the carton’s orientation.
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Identification of the carton’s dimensions. When the carton is deformed, the vision recognition accuracy will be affected. Generally, the dimensions of a carton are recognized by scanning equipment.
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Detection of damage on the carton surface. This solution does not support the defect detection function.