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A Kubernetes-Based Edge Architecture for Controlling the Trajectory of a Resource-Constrained Aerial Robot by Enabling Model Predictive Control
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
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0003-0126-1897
2022 (English)In: Proceedings - 26th International Conference on Circuits, Systems, Communications and Computers, CSCC 2022, Institute of Electrical and Electronics Engineers Inc. , 2022, p. 290-295Conference paper, Published paper (Refereed)
Abstract [en]

In recent years, cloud and edge architectures have gained tremendous focus for offloading computationally heavy applications. From machine learning and Internet of Thing (IOT) to industrial procedures and robotics, cloud computing have been used extensively for data processing and storage purposes, thanks to its 'infinite' resources. On the other hand, cloud computing is characterized by long time delays due to the long distance between the cloud servers and the machine requesting the resources. In contrast, edge computing provides almost real-time services since edge servers are located significantly closer to the source of data. This capability sets edge computing as an ideal option for real-time applications, like high level control, for resource-constrained platforms. In order to utilized the edge resources, several technologies, with basic ones as containers and orchestrators like kubernetes, have been developed to provide an environment with many different features, based on each application's requirements. In this context, this works presents the implementation and evaluation of a novel edge architecture based on kubernetes orchestration for controlling the trajectory of a resource-constrained Unmanned Aerial Vehicle (UAV) by enabling Model Predictive Control (MPC).

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2022. p. 290-295
Keywords [en]
Edge Computing, Kubernetes, MPC, Robotics, UAV
National Category
Computer Sciences Control Engineering Robotics
Research subject
Robotics and Artificial Intelligence
Identifiers
URN: urn:nbn:se:ltu:diva-95672DOI: 10.1109/CSCC55931.2022.00056Scopus ID: 2-s2.0-85147732087ISBN: 978-1-6654-8186-1 (electronic)OAI: oai:DiVA.org:ltu-95672DiVA, id: diva2:1738350
Conference
26th International Conference on Circuits, Systems, Communications and Computers (CSCC 2022), July 19-22, 2022, Crete, Greece
Funder
EU, Horizon 2020, 953454Available from: 2023-02-21 Created: 2023-02-21 Last updated: 2023-09-20Bibliographically 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

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Seisa, Achilleas SantiSatpute, Sumeet GajananNikolakopoulos, George

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