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E-CNMPC: Edge-Based Centralized Nonlinear Model Predictive Control for Multiagent Robotic Systems
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: 0000-0003-3922-1735
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: IEEE Access, E-ISSN 2169-3536, Vol. 10, p. 121590-121601Article in journal (Refereed) Published
Abstract [en]

With the wide deployment of autonomous multi-agent robotic systems, control solutions based on centralized algorithms have been developed. Even though these centralized algorithms can optimize the performance of the multi-agent robotic systems, they require a lot of computational effort, and a centralized unit to undertake the entire process. Yet, many robotic platforms like some ground robots and even more, aerial robots, do not have the computing capacity to execute this kind of frameworks on their onboard computers. While cloud computing has been used as a solution for offloading computationally demanding robotic applications, from the robots to the cloud servers, the latency they introduce to the system has made them unsuitable for time sensitive applications. To overcome these challenges, this article promotes an Edge computing-based Centralized Nonlinear Model Predictive Control (E-CNMPC) framework to control, and optimize, in swarm formation, the trajectory of multiple ground robotic agents, while taking under consideration potential collisions. The data processing procedure for the time critical application of controlling the robots in a centralized manner, is offloaded to the edge machine, thus the framework benefits from the provided edge resources, features, and centralized optimal performance, while the latency remains bounded in desired values. Besides, real experiments were conducted as a proof-of-concept of the proposed framework to evaluate the system’s performance and effectiveness.

Place, publisher, year, edition, pages
IEEE, 2022. Vol. 10, p. 121590-121601
Keywords [en]
Edge-based centralized nonlinear model predictive control (E-CNMPC), edge computing, Kubernetes, robotics
National Category
Robotics and automation Computer Sciences
Research subject
Robotics and Artificial Intelligence
Identifiers
URN: urn:nbn:se:ltu:diva-94383DOI: 10.1109/access.2022.3223446ISI: 000890845000001Scopus ID: 2-s2.0-85142784455OAI: oai:DiVA.org:ltu-94383DiVA, id: diva2:1714827
Funder
EU, Horizon 2020, 953454
Note

Validerad;2022;Nivå 2;2022-11-30 (hanlid)

Available from: 2022-11-30 Created: 2022-11-30 Last updated: 2025-02-05Bibliographically 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 SantiLindqvist, BjörnSatpute, Sumeet GajananNikolakopoulos, George

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