Project Overview
The CMES Palletizing solution is a robot automation system designed to automatically stack boxes of various sizes and shapes onto pallets. This system optimizes palletizing—one of the most critical tasks in Logistics—by integrating 3D vision and AI technology. Even when irregular boxes arrive at random, the AI independently generates optimal stacking patterns, providing high efficiency and space utilization.
Project Background & Objectives
Palletizing is crucial in large-scale logistics centers and manufacturing, but the increasing variety of box sizes and shapes makes manual labor time-consuming and costly. To address this, CMES has developed AI learning algorithms since 2014 and finalized an automated palletizing solution through various verification stages. This solution was specifically implemented to meet the technical need for fast and accurate processing of irregular boxes in large-scale logistics centers like Coupang.
Components
| Robot | Robot Arm: Performs palletizing tasks according to AI commands, safely handling boxes of various sizes and weights with precise movements. |
|---|
Workflow
| STEP 1. | Incoming Box Scan: The 3D vision system scans the size and shape of incoming boxes in real-time. |
|---|---|
| STEP 2. | Palletizing Pattern Generation: The AI analyzes box data to automatically generate the optimal palletizing pattern. |
| STEP 3. | Buffer System Operation: Temporarily stores items in a buffer system if necessary to maximize palletizing efficiency. |
| STEP 4. | Palletizing Execution: The robot arm completes the task by systematically stacking boxes onto the pallet as directed by the AI. |
※ The content provided by Bigwave Robotics is protected under U.S. and Canadian copyright and intellectual property laws. Unauthorized reproduction, distribution, or use of this content is strictly prohibited.
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Project Overview
The CMES Palletizing solution is a robot automation system designed to automatically stack boxes of various sizes and shapes onto pallets. This system optimizes palletizing—one of the most critical tasks in Logistics—by integrating 3D vision and AI technology. Even when irregular boxes arrive at random, the AI independently generates optimal stacking patterns, providing high efficiency and space utilization.
Project Background & Objectives
Palletizing is crucial in large-scale logistics centers and manufacturing, but the increasing variety of box sizes and shapes makes manual labor time-consuming and costly. To address this, CMES has developed AI learning algorithms since 2014 and finalized an automated palletizing solution through various verification stages. This solution was specifically implemented to meet the technical need for fast and accurate processing of irregular boxes in large-scale logistics centers like Coupang.
Components
| Robot | Robot Arm: Performs palletizing tasks according to AI commands, safely handling boxes of various sizes and weights with precise movements. |
|---|
Workflow
| STEP 1. | Incoming Box Scan: The 3D vision system scans the size and shape of incoming boxes in real-time. |
|---|---|
| STEP 2. | Palletizing Pattern Generation: The AI analyzes box data to automatically generate the optimal palletizing pattern. |
| STEP 3. | Buffer System Operation: Temporarily stores items in a buffer system if necessary to maximize palletizing efficiency. |
| STEP 4. | Palletizing Execution: The robot arm completes the task by systematically stacking boxes onto the pallet as directed by the AI. |
※ The content provided by Bigwave Robotics is protected under U.S. and Canadian copyright and intellectual property laws. Unauthorized reproduction, distribution, or use of this content is strictly prohibited.
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Indoor Logistics Transport and Loading Using VisionNav VNPA15🏗️
AMR Logistics Robots Introduced to Manufacturing Factory ✨ Logistics Automation that Quickly Resolves Even 1 Ton of Waste Derbris (2 KUKA KMP 1500P Logistics Robots)