Kill-the-PLC: implementation and performance evaluation of a disruptive robotic cell environment based on 5G and edge-cloud technologies
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Máster Universitario en Ingeniería de Telecomunicación
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This project will investigate a disruptive industrial concept consisting in re-architecting the control network of an operational robotic cell environment (composed by a PLC and robotic arm) applying 5G and edge-cloud technologies. By making use of those technologies, the cell processing and computing power, which is currently done by the PLC on-site, could be offloaded to a centralized edge-cloud location, allowing for mass-offloading, optimization, and flexibilization of the industrial production process. The student is expected to analyze and understand the functioning of the current robotic cell both from a communications and industrial production point of view, identifying the different PLC and robotic control interfaces and their associated communication protocols and requirements. Based on the initial analysis, the student will propose one or several architectures for “cloudified” operation using edge-cloud capabilities and communication over 5G technology. The student is also expected to implement and validate the proposed architectures. The project will investigate the use and performance of industrial communication protocols such as MQTT and OPC-UA for optimized performance in live operational conditions. The proposed activities will take place under the umbrella of the “5G-ROBOT: 5G-Enabled Autonomous Mobile Robotic Systems” Danish national project, and the student is expected to disseminate the ongoing work and learnings both internally and externally towards industrial partners. The experimental work will take place at the AAU 5G Smart Production Lab, where 5G SA NPN Rel. 15/16 with FR1 and FR2 support and edge-computing are available for industrial research and live testing in operational conditions. The student will work in close cooperation with other students, expert researchers in the areas of robotics, industrial manufacturing, and communications, and representatives from industrial partners.
This project will investigate a disruptive industrial concept consisting in re-architecting the control network of an operational robotic cell environment (composed by a PLC and robotic arm) applying 5G and edge-cloud technologies. By making use of those technologies, the cell processing and computing power, which is currently done by the PLC on-site, could be offloaded to a centralized edge-cloud location, allowing for mass-offloading, optimization, and flexibilization of the industrial production process. The student is expected to analyze and understand the functioning of the current robotic cell both from a communications and industrial production point of view, identifying the different PLC and robotic control interfaces and their associated communication protocols and requirements. Based on the initial analysis, the student will propose one or several architectures for “cloudified” operation using edge-cloud capabilities and communication over 5G technology. The student is also expected to implement and validate the proposed architectures. The project will investigate the use and performance of industrial communication protocols such as MQTT and OPC-UA for optimized performance in live operational conditions. The proposed activities will take place under the umbrella of the “5G-ROBOT: 5G-Enabled Autonomous Mobile Robotic Systems” Danish national project, and the student is expected to disseminate the ongoing work and learnings both internally and externally towards industrial partners. The experimental work will take place at the AAU 5G Smart Production Lab, where 5G SA NPN Rel. 15/16 with FR1 and FR2 support and edge-computing are available for industrial research and live testing in operational conditions. The student will work in close cooperation with other students, expert researchers in the areas of robotics, industrial manufacturing, and communications, and representatives from industrial partners.
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- Trabajos Fin de Máster [5220]
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