Predict Pick Points V2
Function
This Step recognizes the pick-able objects based on the 2D images and depth maps and outputs the corresponding pick points.
Usage Scenario
This Step is designed for piece picking in logistics, supermarket, and cables industry. This Step follows the Scale Image in 2D ROI Step to obtain the information of the scaled depth map, point cloud, and ROI.
Requirement of Graphics Card
This Step requires a graphics card of NVIDIA GTX 1650 Ti or higher to be used.
Instructions for Use
Before using this Step, please wait for the deep learning server to start. If the deep learning server is started successfully, a message saying that Deep learning server started successfully at xxx will appear in the log panel, and then you can run the Step.
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When running this Step for the first time, you should load a Picking Configuration File.
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When you run this Step for the first time, the deep learning model will be optimized according to the hardware type and the one-time optimization process takes about 10 to 30 minutes depending on the computer configuration. Please wait for a while. After the model is optimized, the execution time of the Step will be greatly reduced.
Parameter Description
Server
- Server IP
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Description: This parameter is used to set the IP address of the deep learning server.
Default value: 127.0.0.1
Tuning instruction: Please set the parameter according to the actual requirement.
- Server Port (1–65535)
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Description: This parameter is used to set the port number of the deep learning server.
Default value: 60054
Value range: 60000–65535
Tuning instruction: Please set the parameter according to the actual requirement.
Picking Configuration
- Picking Configuration Folder Path
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Description: This parameter is used to select the path where the picking configuration folder is stored.
Tuning recommendation: Before you run the project, please load the Picking Configuration Folder first. We provides five types of picking configuration files used for logistics (semantic segmentation), logistics (object detection), supermarket, cables, and pharmaceutical industry, as shown in the table below. Please contact Mech-Mind Technical Support to request the model files you need first.
| Usage Scenario | Picking Configuration Folder Name |
|---|---|
Logistics (semantic segmentation) |
Logistics_Seg_RGBSuction |
Logistics (object detection) |
Logistics_OD_RGBSuction |
Supermarket |
Supermarket_Seg_RGBSuction |
Cables |
Cable_Seg_RGBGrasp |
Medicine Boxes |
MedicineBox_Instance_3DSize_RGBSuction |
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There are two JSON files and one model folder in the picking configuration folder. The deep learning model is stored in the model folder. The folder path should NOT contain the model folder, or else this Step cannot function properly. For example, a correct path can be D:/ConfigurationFiles/Cable_Seg_RGBGrasp. |
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If you are not sure about which type of deep learning model you should use, you can consult Mech-Mind Technical Support for some advice. |
- Logistics (semantic segmentation)
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Please refer to Parameter Adjustment in the Logistics (Semantic Segmentation) Scenario.
- Logistics (object detection)
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Please refer to Parameter Adjustment in the Logistics (Object Detection) Scenario.
- Supermarket
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Please refer to Parameter Adjustment in the Supermarket Scenario.
- Cables
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Please refer to Parameter Adjustment in the Cables Scenario.
- Medicine Boxes
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Please refer to Parameter Adjustment in the Medicine Boxes Scenario.
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It is recommended to use a GeForce GTX 10 Series graphics card with a memory of at least or above 4G when you use the model for the above scenarios. When you run this Step for the first time, the deep learning model will be optimized according to the hardware type and the one-time optimization process takes about 10 to 30 minutes. |