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Communication-Aware Control of Large Data Transmissions via Centralized Cognition and 5G Networks for Multi-Robot Map merging
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. Ericsson Research, Ericsson, Laboratoriegränd 11, Luleå, 977 53, Norrbotten, Sweden.ORCID iD: 0000-0003-0570-0447
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0002-0108-6286
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-8235-2728
Ericsson Research, Ericsson, Laboratoriegränd 11, Luleå, 977 53, Norrbotten, Sweden.
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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. Vol. 110, no 1, article id 22
Keywords [en]
5G, Edge, Map merging, Multi-agent
National Category
Communication Systems
Research subject
Robotics and Artificial Intelligence
Identifiers
URN: urn:nbn:se:ltu:diva-103941DOI: 10.1007/s10846-023-02045-4ISI: 001148732200002Scopus ID: 2-s2.0-85182956096OAI: oai:DiVA.org:ltu-103941DiVA, id: diva2:1831596
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
In thesis
1. 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

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Damigos, GerasimosStathoulopoulos, NikolaosKoval, AntonNikolakopoulos, George

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