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An Edge-Based Architecture for Offloading Model Predictive Control for UAVs
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: 0000-0003-3922-1735
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0003-0126-1897
2022 (English)In: Robotics, E-ISSN 2218-6581, Vol. 11, no 4, article id 80Article in journal (Refereed) Published
Abstract [en]

Thanks to the development of 5G networks, edge computing has gained popularity in several areas of technology in which the needs for high computational power and low time delays are essential. These requirements are indispensable in the field of robotics, especially when we are thinking in terms of real-time autonomous missions in mobile robots. Edge computing will provide the necessary resources in terms of computation and storage, while 5G technologies will provide minimal latency. High computational capacity is crucial in autonomous missions, especially for cases in which we are using computationally demanding high-level algorithms. In the case of Unmanned Aerial Vehicles (UAVs), the onboard processors usually have limited computational capabilities; therefore, it is necessary to offload some of these tasks to the cloud or edge, depending on the time criticality of the application. Especially in the case of UAVs, the requirement to have large payloads to cover the computational needs conflicts with other payload requirements, reducing the overall flying time and hindering autonomous operations from a regulatory perspective. In this article, we propose an edge-based architecture for autonomous UAV missions in which we offload the high-level control task of the UAV’s trajectory to the edge in order to take advantage of the available resources and push the Model Predictive Controller (MPC) to its limits. Additionally, we use Kubernetes to orchestrate our application, which runs on the edge and presents multiple experimental results that prove the efficacy of the proposed novel scheme.

Place, publisher, year, edition, pages
MDPI, 2022. Vol. 11, no 4, article id 80
Keywords [en]
edge computing, UAV, model predictive control, Kubernetes
National Category
Robotics and automation
Research subject
Robotics and Artificial Intelligence
Identifiers
URN: urn:nbn:se:ltu:diva-92475DOI: 10.3390/robotics11040080ISI: 000845262500001Scopus ID: 2-s2.0-85137000114OAI: oai:DiVA.org:ltu-92475DiVA, id: diva2:1687290
Note

Validerad;2022;Nivå 2;2022-08-16 (sofila)

Available from: 2022-08-15 Created: 2022-08-15 Last updated: 2025-02-09Bibliographically 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 and automation
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: 2025-02-09Bibliographically approved

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Seisa, Achilleas SantiSatpute, Sumeet GajananLindqvist, BjörnNikolakopoulos, George

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