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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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Published on: October 14, 2017

Digital Twin for Automatic Transportation in Industry 4.0.

Alberto Martínez-Gutiérrez¹, Javier Díez-González¹, Rubén Ferrero-Guillén¹

¹Department of Mechanical, Computer and Aerospace Engineering, Universidad de León, 24071 León, Spain.

Sensors (Basel, Switzerland)

|June 2, 2021

Summary

This study introduces a novel Digital Twin (DT) design for optimizing Automatic Guided Vehicle (AGV) logistics in Smart Manufacturing (SM). Real-world experiments validate the DT model, achieving high accuracy in predicting AGV navigation performance.

Keywords:

AGV Digital Twin Industrial Ethernet Industry 4.0 MIR100 ROS cloud computing hyperconnectivity simulation smart manufacturing

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Area of Science:

Manufacturing Engineering
Industrial Automation
Computer Science

Background:

Industry 4.0 drives the digitalization of manufacturing processes, with Smart Manufacturing (SM) encompassing key areas like real-time data acquisition and virtualization.
Digital Twins (DT) are gaining research traction for simulating industrial plant dynamics, enabling cost reduction and problem prediction within SM.
Automatic Guided Vehicles (AGVs) are increasingly adopted for material handling and Material Requirement Planning (MRP) in collaborative industrial environments.

Purpose of the Study:

To propose a novel Digital Twin (DT) design concept for enhancing the transportation services of Automatic Guided Vehicles (AGVs).
To validate the proposed DT model through real-world experimentation in a Smart Manufacturing (SM) context.
To assess the accuracy and effectiveness of the DT in predicting AGV navigation performance.

Main Methods:

Development of a new Digital Twin (DT) design focused on external services for AGV transportation.
Implementation of an Industrial Ethernet platform for real-time data acquisition and system control.
Conducting real-world experiments in two distinct industrial scenarios to validate the DT model.

Main Results:

The Digital Twin (DT) model demonstrated a high correlation with real-world experiments.
Validation results showed an accuracy of 97.95% and 98.82% in predicting the total mission time for AGVs.
The proposed DT design effectively models and predicts AGV navigation within the industrial plant.

Conclusions:

The developed Digital Twin (DT) model successfully validates AGV navigation in a Smart Manufacturing (SM) setting.
The high accuracy achieved confirms the utility of the DT for simulating and optimizing AGV operations.
This research contributes to the advancement of Industry 4.0 by providing a validated DT solution for intelligent logistics.