Process layout
Project Overview
This video presents an autonomous mobile robot (AMR) logistics transport system based on SLAM (Simultaneous Localization and Mapping). It provides an optimal solution for logistics and precision repetitive tasks on production sites by implementing autonomous unmanned transport and loading. Key features include Laser Sensing-based self-mapping and optimal path securing with safety through 2D/3D cameras for object recognition and obstacle identification. The system can be equipped with various applications such as collaborative robots (Co-Bots), roller conveyors, and Jack-Up systems.
What is SLAM (Simultaneous Localization and Mapping)? It is a core technology for autonomous driving where a robot creates an accurate map of its work environment using only its onboard autonomous sensors while navigating the workspace.
Project Background & Objectives
Accurate and repetitive logistics tasks using autonomous robots.
Components
| Robot |
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Workflow
| STEP 1. | Scenario Configuration: Configure transport and loading scenarios according to the work environment. |
|---|---|
| STEP 2. | System Integration: Install and connect the system to the site. |
| STEP 3. | SLAM Mapping: Create a work map using the AMR’s built-in SLAM function. |
| STEP 4. | Site Application: Implement the system for operational use. |
Features
Operational Efficiency and Safety
Market Proven AMR: Secured stability with the world's No. 1 market share in autonomous mobile robots.
Efficient Space Utilization and Optimal Logistics Routing: Applied SLAM-based 3D mapping and Digital Twin CCTV monitoring.
Max 12–18 Months ROI
Labor replacement effect for loading and transport tasks.
Labor costs (including incidental expenses) X 2 Shifts = 90–100 million KRW per year.
Flexibility in human resource management and operations.
Warranty Period: 1 year provided.
Results
| Key Benefits | Reduced operational losses + reduced accidents = increased opportunity cost.
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|---|---|
| Client Feedback | "Thanks to the outstanding performance of the logistics robots, we were able to configure stable and accurate logistics scenarios."
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