This solution utilizes 3D vision, deep learning algorithms (Instance Segmentation), reinforcement learning, and motion planning (OMPL) algorithms to automate tasks such as product sorting, assembly, and packing.
By using cost-effective 3D vision to recognize product types, product locations, and box positions in real-time, the system can automatically compensate for and continue operations even if the positions of products or boxes change.
Furthermore, with two vision sensors and high-speed processing, it can significantly reduce cycle times—the most critical factor in logistics sites.
Since the system compensates through learning even if the conditions change due to misalignment of the vision, robot, or work jigs, it ensures easy operation and maintenance while minimizing downtime.
Components
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Workflow
| STEP 1. | Separately recognize each product from an input box containing a mix of various items. |
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| STEP 2. | Pick products starting from those stacked on top. |
| STEP 3. | Load the picked products into designated positions within the discharge box. |
| STEP 4. | Continue operations until all items in the input box are sorted into individual boxes. (The system recognizes Pick & Place locations via vision and performs tasks regardless of changes in the positions of input or discharge boxes.) |
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Non-Stop Product Sorting Automation using Robostar RA007 and Deep Learning
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- Estimated Project Duration
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- Robot Model
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