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Toward 5G Edge Computing for Enabling Autonomous Aerial Vehicles
Ericsson Research, Luleå, Sweden.ORCID iD: 0000-0003-0570-0447
Ericsson Research, Luleå, Sweden.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0003-0126-1897
2023 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 11, p. 3926-3941Article in journal (Refereed) Published
Abstract [en]

Offloading processes responsible for a robot’s control operation to external computational resources has been in the spotlight for many years. The vision of having access to a full cloud cluster for any autonomous robot has fueled many scientific fields. Such implementations rely strongly on a robust communication link between the robot and the cloud and have been tested over numerous network architectures. However, various limitations have been highlighted upon the realization of such platforms. For small-scale local deployments, technologies such as Wi-Fi, Zigbee, and blacktooth are inexpensive and easy to use but suffer from low transmit power and outdoor coverage limitations. In this study, the offloading time-critical control operations for an unmanned aerial vehicle (UAV) using cellular network technologies were evaluated and demonstrated experimentally, focusing on the 5G technology. The control process was hosted on an edge server that served as a ground control station (GCS). The server performs all the computations required for the autonomous operation of the UAV and sends the action commands back to the UAV over the 5G interface. This research focuses on analyzing the low-latency needs of a closed-loop control system that is put to the test on a real 5G network. Furthermore, practical limitations, integration challenges, the intended cellular architecture, and the corresponding Key Performance Indicators (KPIs) that correlate to the real-life behavior of the UAV are rigorously studied.

Place, publisher, year, edition, pages
IEEE, 2023. Vol. 11, p. 3926-3941
Keywords [en]
5G, edge computing, robotics, UAV
National Category
Robotics Communication Systems
Research subject
Robotics and Artificial Intelligence
Identifiers
URN: urn:nbn:se:ltu:diva-95577DOI: 10.1109/access.2023.3235067ISI: 000915766800001Scopus ID: 2-s2.0-85147271758OAI: oai:DiVA.org:ltu-95577DiVA, id: diva2:1735728
Funder
EU, Horizon 2020, 953454
Note

Validerad;2023;Nivå 2;2023-02-09 (joosat);

Licens fulltext: CC BY License

Available from: 2023-02-09 Created: 2023-02-09 Last updated: 2024-04-15Bibliographically approved
In thesis
1. Towards 5G-Enabled Intelligent Machines
Open this publication in new window or tab >>Towards 5G-Enabled Intelligent Machines
2024 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis introduces a novel framework for enabling intelligent machines and robots with the fifth-generation (5G) cellular network technology. Autonomous robots, such as Unmanned Aerial Vehicles (UAVs), Autonomous Guided Vehicles (AGVs), and more, can notably benefit from multi-agent collaboration, human supervision, or operation guidance, as well as from external computational units such as cloud edge servers, in all of which a framework to utilize reliable communication infrastructure is needed. Autonomous robots are often employed to alleviate humans by operating demanding missions such as inspection and data collection in harsh environments or time-critical operations in industrial environments - to name a few. For delivering data to other robots to maximize the effectiveness of the considered mission, for executing complex algorithms by offloading them into the edge cloud, or for including a human operator/supervisor into the loop, the 5G network and its advanced Quality of Service (QoS) features can be employed to facilitate the establishment of such a framework. This work focuses on establishing a baseline for integrating various time-critical robotics platforms and applications with a 5G network. These applications include offloading computationally intensive Model Predictive Control (MPC) algorithms for trajectory tracking of UAVs into the edge cloud, adapting data sharing in multi-robot systems based on network conditions, and enhancing network-aware surrounding autonomy components. We have identified a set of key performance indicators (KPIs) crucially affecting the performance of network-dependent robots and applications. We have proposed novel solutions and mechanisms to meet these requirements, which aim to combine traditional robotics techniques to enhance mission reliability with the exploitation of 5G features such as the QoS framework. Ultimately, our goal was to develop solutions that adhere to the essential paradigm of co-designing robotics with networks. We thoroughly evaluated all presented research using real-life platforms and 5G networks.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2024
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
Keywords
UAV, 5G, QoS, offloading
National Category
Robotics
Research subject
Robotics and Artificial Intelligence
Identifiers
urn:nbn:se:ltu:diva-105110 (URN)978-91-8048-536-4 (ISBN)978-91-8048-537-1 (ISBN)
Presentation
2024-05-29, F341, Luleå University of Technology, Luleå, 09:00 (English)
Opponent
Supervisors
Available from: 2024-04-16 Created: 2024-04-15 Last updated: 2024-05-08Bibliographically approved

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