Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A Resilient Framework for 5G-Edge-Connected UAVs based on Switching Edge-MPC and Onboard-PID Control
Ericsson Research, Luleå, Sweden.ORCID iD: 0000-0003-0570-0447
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-9685-1026
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0003-1437-1809
Ericsson Research, Luleå, Sweden.
Show others and affiliations
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: urn:nbn:se:ltu:diva-101398DOI: 10.1109/ISIE51358.2023.10228114Scopus ID: 2-s2.0-85172099523OAI: oai:DiVA.org:ltu-101398DiVA, id: diva2:1799032
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
In thesis
1. Towards Enabling the Next Generation of Edge Controlled Robotic Systems
Open this publication in new window or tab >>Towards Enabling the Next Generation of Edge Controlled Robotic Systems
2023 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis introduces a novel framework for edge robotics, enabling the advancement of edge-connected and controlled robots. Autonomous robots, such as Unmanned Aerial Vehicles (UAVs), generate vast amounts of multi-sensor data and rely on complex algorithms. However, their computational requirements often necessitate large onboard computing units, limiting their flight time and payload capacity. This work presents a key contribution towards the development of frameworks that facilitate offloading computational processes from robots to edge computing clusters. Specifically, we focus on offloading computationally intensive Model Predictive Control (MPC) algorithms for UAV trajectory control. To address the time-critical nature of these procedures, we also consider latency and safety measures. By leveraging edge computing, we can achieve the required computational capacity while minimizing communication latency, making it a promising solution for such missions. Furthermore, edge computing enhances the performance and efficiency of MPCs compared to traditional onboard computers. We evaluate this improvement and compare it to conventional approaches. Additionally, we leverage Docker Images and Kubernetes Clusters to take advantage of their features, enabling fast and easy deployment, operability, and migrations of the MPC instances. Kubernetes automates, monitors, and orchestrates the system’s behavior, while the controller applications become highly portable without extensive software dependencies. This thesis focuses on developing real architectures for offloading MPCs either for controlling the trajectory of single robots or multi-agent systems, while utilizing both on-premises small-scale edge computing setups and edge computing providers like the Research Institutes of Sweden (RISE) in Luleå. Extensive simulations and real-life experimental setups support the results and assumptions presented in this work.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2023
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
Keywords
Edge Robotics, Edge Computing, Robotics
National Category
Robotics
Research subject
Robotics and Artificial Intelligence
Identifiers
urn:nbn:se:ltu:diva-101400 (URN)978-91-8048-379-7 (ISBN)978-91-8048-380-3 (ISBN)
Presentation
2023-11-14, A1547, Luleå tekniska universitet, Luleå, 09:00 (English)
Opponent
Supervisors
Available from: 2023-09-21 Created: 2023-09-20 Last updated: 2023-10-24Bibliographically approved
2. 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

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Seisa, Achilleas SantiSatpute, Sumeet GajananNikolakopoulos, George

Search in DiVA

By author/editor
Damigos, GerasimosSeisa, Achilleas SantiSatpute, Sumeet GajananNikolakopoulos, George
By organisation
Signals and Systems
RoboticsControl Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 50 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf