Vision-Guided Loading Randomly Stacked Target Objects
This tutorial introduces how to deploy a 3D vision–guided random bin picking application, using the application template case “Loading Randomly Stacked Target Objects” in the Solution Library.
Application scenario: The 3D vision system guides the robot to pick target objects randomly stacked in bins or pallets and place them on conveyor lines/secondary positioning platforms, tipping platforms, and so on.
Application Overview
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Target object: target objects that are randomly stacked. The application uses bolts as an example.
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This application uses the CAD model file of the target object to make the target object model. Therefore, you need to prepare a .stl model file for the target object in advance. You can download it by clicking here.
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This application uses a real camera to capture images of the bolts for target object recognition. If you want to use a virtual camera, please click here to download image data of the bolts.
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This application uses deep learning for recognition assistance, so you need to prepare a deep learning model package for deep learning inference. You can click here to download the trained deep learning model package.
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Camera: Mech-Eye PRO S camera, mounted in eye to hand (ETH) mode.
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Calibration board: When the working distance is 500 to 800 mm, it is recommended to use the calibration board model CGB-035; when the working distance is 800 to 1000 mm, it is recommended to use the calibration board CGB-035.
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Robot: a six-axis robot. This application uses FANUC_LR_MATE_200ID as an example.
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IPC: Mech-Mind IPC STD
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Software: Mech-Vision & Mech-Viz 2.0.0, Mech-Eye Viewer 2.4.0, Mech-DLK 2.6.0
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Communication solution: Standard Interface communication, in which the vision system outputs the path planned by the Mech-Viz software.
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End tool: gripper
For this application, you are required to prepare a model file in .obj format for the gripper, which will be used for collision detection during path planning. You can download it by clicking here.
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Scene object: scene object model
This application requires a scene model file in .stl format, which is used to simulate a real scene and is used for collision detection in path planning. You can download it by clicking here.
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If you are using a different camera model, robot brand, or target object than in this example, please refer to the reference information provided in the corresponding steps to make adjustments. |
Deploy a Vision-Guided Robotic Application
The deployment of the vision-guided robotic application can be divided into six phases, as shown in the figure below:
The following table describes the six phases of deploying a vision-guided robotic application.
| No. | Phase | Description |
|---|---|---|
1 |
Vision Solution Design |
Select the hardware model according to the project requirements, determine the mounting mode, vision processing method, etc. (This tutorial has a corresponding vision solution, and you do not need to design it yourself.) |
2 |
Install and connect hardware of the Mech-Mind Vision System. |
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3 |
Load the robot master-control program and the configuration files to the robot system and set up the communication between the vision system and the robot, thus helping the Mech-Mind Vision System obtain control over the robot. |
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4 |
Perform the automatic hand-eye calibration in the eye-to-hand setup, to establish the transformation relationship between the camera reference frame and the robot reference frame. |
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5 |
Use the application template “Loading Randomly Stacked Target Objects” in Mech-Vision Solution Library and plan the robot path with the advanced component of path planning. |
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6 |
Based on the robot example program MM_S2_Viz_Basic, write a pick-and-place program suitable for on-site applications. |
Next, follow subsequent sections to complete the application deployment.