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Towards 5G-Enabled Intelligent Machines
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. Ericsson AB.ORCID iD: 0000-0003-0570-0447
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 [en]
UAV, 5G, QoS, offloading
National Category
Robotics
Research subject
Robotics and Artificial Intelligence
Identifiers
URN: urn:nbn:se:ltu:diva-105110ISBN: 978-91-8048-536-4 (print)ISBN: 978-91-8048-537-1 (electronic)OAI: oai:DiVA.org:ltu-105110DiVA, id: diva2:1851743
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
List of papers
1. Edge Computing Architectures for Enabling the Realisation of the Next Generation Robotic Systems
Open this publication in new window or tab >>Edge Computing Architectures for Enabling the Realisation of the Next Generation Robotic Systems
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2022 (English)In: 2022 30th Mediterranean Conference on Control and Automation (MED), IEEE, 2022, p. 487-493Conference paper, Published paper (Refereed)
Abstract [en]

Edge Computing is a promising technology toprovide new capabilities in technological fields that require instantaneous data processing. Researchers in areas such asmachine and deep learning use extensively edge and cloud computing for their applications, mainly due to the significant computational and storage resources that they provide. Currently, Robotics is seeking to take advantage of these capabilities as well, and with the development of 5G networks, some existing limitations in the field can be overcome. In this context, it is important to know how to utilize the emerging edge architectures, what types of edge architectures and platforms exist today and which of them can and should be used based on each robotic application. In general, Edge platforms can be implemented and used differently, especially since there are several providers offering more or less the same set of serviceswith some essential differences. Thus, this study addresses these discussions for those who work in the development of the next generation robotic systems and will help to understand the advantages and disadvantages of each edge computing architecture in order to choose wisely the right one for each application.

Place, publisher, year, edition, pages
IEEE, 2022
Series
Mediterranean Conference on Control and Automation (MED), ISSN 2325-369X, E-ISSN 2473-3504
Keywords
Robotics, Edge Computing
National Category
Control Engineering
Research subject
Robotics and Artificial Intelligence
Identifiers
urn:nbn:se:ltu:diva-92485 (URN)10.1109/MED54222.2022.9837289 (DOI)000854013700081 ()2-s2.0-85136265211 (Scopus ID)
Conference
30th Mediterranean Conference on Control and Automation (MED), Vouliagmeni, Greece, June 28 - July 1, 2022
Projects
AERO-TRAIN
Funder
EU, Horizon 2020, 953454
Note

ISBN för värdpublikation: 978-1-6654-0673-4 (electronic), 978-1-6654-0674-1 (print)

Available from: 2022-08-15 Created: 2022-08-15 Last updated: 2024-04-15Bibliographically approved
2. Toward 5G Edge Computing for Enabling Autonomous Aerial Vehicles
Open this publication in new window or tab >>Toward 5G Edge Computing for Enabling Autonomous Aerial Vehicles
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
Keywords
5G, edge computing, robotics, UAV
National Category
Robotics Communication Systems
Research subject
Robotics and Artificial Intelligence
Identifiers
urn:nbn:se:ltu:diva-95577 (URN)10.1109/access.2023.3235067 (DOI)000915766800001 ()2-s2.0-85147271758 (Scopus ID)
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
3. Performance of Sensor Data Process Offloading on 5G-Enabled UAVs
Open this publication in new window or tab >>Performance of Sensor Data Process Offloading on 5G-Enabled UAVs
2023 (English)In: Sensors, E-ISSN 1424-8220, Vol. 23, no 2, article id 864Article in journal (Refereed) Published
Abstract [en]

Recently, unmanned aerial vehicle (UAV)-oriented applications have been growing worldwide. Thus, there is a strong interest in using UAVs for applications requiring wide-area connectivity coverage. Such applications might be power line inspection, road inspection, offshore site monitoring, wind turbine inspections, and others. The utilization of cellular networks, such as the fifth-generation (5G) technology, is often considered to meet the requirement of wide-area connectivity. This study quantifies the performance of 5G-enabled UAVs when sensor data throughput requirements are within the 5G network’s capability and when throughput requirements significantly exceed the capability of the 5G network, respectively. Our experimental results show that in the first case, the 5G network maintains bounded latency, and the application behaves as expected. In the latter case, the overloading of the 5G network results in increased latency, dropped packets, and overall degradation of the application performance. Our findings show that offloading processes requiring moderate sensor data rates work well, while transmitting all the raw data generated by the UAV’s sensors is not possible. This study highlights and experimentally demonstrates the impact of critical parameters that affect real-life 5G-enabled UAVs that utilize the edge-offloading power of a 5G cellular network.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
UAV, 5G, offloading, sensors
National Category
Communication Systems
Research subject
Robotics and Artificial Intelligence
Identifiers
urn:nbn:se:ltu:diva-104925 (URN)10.3390/s23020864 (DOI)000918867000001 ()36679660 (PubMedID)2-s2.0-85146702138 (Scopus ID)
Funder
EU, Horizon 2020, 953454
Note

Godkänd;2024;Nivå 0;2024-04-02 (signyg);

Full text license: CC BY

Available from: 2024-04-01 Created: 2024-04-01 Last updated: 2024-04-19Bibliographically approved
4. PACED-5G: Predictive Autonomous Control using Edge for Drones over 5G
Open this publication in new window or tab >>PACED-5G: Predictive Autonomous Control using Edge for Drones over 5G
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2023 (English)In: 2023 International Conference on Unmanned Aircraft Systems (ICUAS), IEEE, 2023, p. 1155-1161Conference paper, Published paper (Refereed)
Abstract [en]

With the advent of technologies such as Edge computing, the horizons of remote computational applications have broadened multi-dimensionally. Autonomous Unmanned Aerial Vehicle (UAV) mission is a vital application to utilize remote computation to catalyze its performance. However, offloading computational complexity to a remote system increases the latency in the system. Though technologies such as 5G networking minimize communication latency, the effects of latency on the control of UAVs are inevitable and may destabilize the system. Hence, it is essential to consider the delays in the system and compensate for them in the control design. Therefore, we propose a novel Edge-based predictive control architecture enabled by 5G networking, PACED-5G (Predictive Autonomous Control using Edge for Drones over 5G). In the proposed control architecture, we have designed a state estimator for estimating the current states based on the available knowledge of the time-varying delays, devised a Model Predictive controller (MPC) for the UAV to track the reference trajectory while avoiding obstacles, and provided an interface to offload the high-level tasks over Edge systems. The proposed architecture is validated in two experimental test cases using a quadrotor UAV.

Place, publisher, year, edition, pages
IEEE, 2023
Series
International Conference on Unmanned Aircraft Systems, ISSN 2373-6720, E-ISSN 2575-7296
National Category
Communication Systems Computer Vision and Robotics (Autonomous Systems)
Research subject
Robotics and Artificial Intelligence
Identifiers
urn:nbn:se:ltu:diva-99487 (URN)10.1109/ICUAS57906.2023.10156241 (DOI)001032475700157 ()2-s2.0-85165619191 (Scopus ID)979-8-3503-1038-2 (ISBN)979-8-3503-1037-5 (ISBN)
Conference
2023 International Conference on Unmanned Aircraft Systems (ICUAS), June 6-9, 2023, Warsaw, Poland
Funder
EU, Horizon 2020, 953454
Available from: 2023-08-10 Created: 2023-08-10 Last updated: 2024-04-15Bibliographically approved
5. A Resilient Framework for 5G-Edge-Connected UAVs based on Switching Edge-MPC and Onboard-PID Control
Open this publication in new window or tab >>A Resilient Framework for 5G-Edge-Connected UAVs based on Switching Edge-MPC and Onboard-PID Control
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2023 (English)In: 2020 IEEE 32nd International Symposium on Industrial Electronics (ISIE): Proceedings, IEEE, 2023Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2023
Series
IEEE International Symposium on Industrial Electronics (ISIE)
National Category
Robotics Control Engineering
Research subject
Robotics and Artificial Intelligence
Identifiers
urn:nbn:se:ltu:diva-101398 (URN)10.1109/ISIE51358.2023.10228114 (DOI)2-s2.0-85172099523 (Scopus ID)
Conference
2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE), Helsiniki-Espoo, Finland, June 19-21, 2023
Funder
EU, Horizon 2020, 953454
Note

ISBN for host publication: 979-8-3503-9971-4

Available from: 2023-09-20 Created: 2023-09-20 Last updated: 2024-04-15Bibliographically approved
6. Communication-Aware Control of Large Data Transmissions via Centralized Cognition and 5G Networks for Multi-Robot Map merging
Open this publication in new window or tab >>Communication-Aware Control of Large Data Transmissions via Centralized Cognition and 5G Networks for Multi-Robot Map merging
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2024 (English)In: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 110, no 1, article id 22Article in journal (Refereed) Published
Abstract [en]

Multiple modern robotic applications benefit from centralized cognition and processing schemes. However, modern equipped robotic platforms can output a large amount of data, which may exceed the capabilities of modern wireless communication systems if all data is transmitted without further consideration. This research presents a multi-agent, centralized, and real-time 3D point cloud map merging scheme for ceaselessly connected robotic agents. Centralized architectures enable mission awareness to all agents at all times, making tasks such as search and rescue more effective. The centralized component is placed on an edge server, ensuring low communication latency, while all agents access the server utilizing a fifth-generation (5G) network. In addition, the proposed solution introduces a communication-aware control function that regulates the transmissions of map instances to prevent the creation of significant data congestion and communication latencies as well as address conditions where the robotic agents traverse in limited to no coverage areas. The presented framework is agnostic of the used localization and mapping procedure, while it utilizes the full power of an edge server. Finally, the efficiency of the novel established framework is being experimentally validated based on multiple scenarios.

Place, publisher, year, edition, pages
Springer, 2024
Keywords
5G, Edge, Map merging, Multi-agent
National Category
Communication Systems
Research subject
Robotics and Artificial Intelligence
Identifiers
urn:nbn:se:ltu:diva-103941 (URN)10.1007/s10846-023-02045-4 (DOI)001148732200002 ()2-s2.0-85182956096 (Scopus ID)
Funder
EU, Horizon 2020, 953454
Note

Validerad;2024;Nivå 2;2024-01-26 (joosat);

Full text: CC BY 4.0 License

Available from: 2024-01-26 Created: 2024-01-26 Last updated: 2024-04-15Bibliographically approved
7. Environmental Awareness Dynamic 5G QoS for Retaining Real Time Constraints in Robotic Applications
Open this publication in new window or tab >>Environmental Awareness Dynamic 5G QoS for Retaining Real Time Constraints in Robotic Applications
2024 (English)Conference paper, Oral presentation with published abstract (Other academic)
National Category
Computer Sciences
Research subject
Robotics and Artificial Intelligence
Identifiers
urn:nbn:se:ltu:diva-105103 (URN)
Conference
2024 IEEE International Conference on Robotics and Automation, May 13-17, 2024, Yokohama, Japan
Funder
EU, Horizon 2020, 953454
Available from: 2024-04-15 Created: 2024-04-15 Last updated: 2024-04-16

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