5G-Powered Autonomous Material Transport in Factories
Canamusa, Premton (2024)
2024
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024060521091
https://urn.fi/URN:NBN:fi:amk-2024060521091
Tiivistelmä
This thesis mapped out and documented the automation of factory tasks using Machine Learning and 5G technology. It highlights the benefits of integrating Machine Learning in robotics, with a 5G-powered envi-ronment.
The thesis focused on the importance of Reinforcement Learning and Object Detection models for achieving full automation. It also covered the crucial role of networking by documenting the creation and maintenance of real-time communication between three robots using the 5G network. To illustrate 5G's role, a comparison between 5G and LTE was made, showing 5G's superiority in speed and reliability. The report further discusses the application and scalability of automation in factories.
The methodology was primarily trial and error, involving fine-tuning AI models and setting up the networking system. The results demonstrated a successful attempt at automating material transportation in factories and showcased the benefits of modern technologies like Artificial Intelligence and 5G.
The thesis focused on the importance of Reinforcement Learning and Object Detection models for achieving full automation. It also covered the crucial role of networking by documenting the creation and maintenance of real-time communication between three robots using the 5G network. To illustrate 5G's role, a comparison between 5G and LTE was made, showing 5G's superiority in speed and reliability. The report further discusses the application and scalability of automation in factories.
The methodology was primarily trial and error, involving fine-tuning AI models and setting up the networking system. The results demonstrated a successful attempt at automating material transportation in factories and showcased the benefits of modern technologies like Artificial Intelligence and 5G.