Qin, C., Candan, F., Mihaylova, L. orcid.org/0000-0001-5856-2223 et al. (1 more author) (2024) 3, 2, 1, Drones go! A testbed to take off UAV swarm intelligence for distributed sensing. In: Panoutsos, G., Mahfouf, M. and Mihaylova, L.S., (eds.) Advances in Computational Intelligence Systems. UKCI 2022. UKCI'2022 - 21st UK Workshop on Computational Intelligence, 07-09 Sep 2022, Sheffield, UK. Advances in Intelligent Systems and Computing, 1454 . Springer Nature , pp. 576-587. ISBN 978-3-031-55567-1
Abstract
This paper introduces a testbed to study distributed sensing problems of Unmanned Aerial Vehicles (UAVs) exhibiting swarm intelligence. Several Smart City applications, such as transport and disaster response, require efficient collection of sensor data by a swarm of intelligent and cooperative UAVs. This often proves to be too complex and costly to study systematically and rigorously without compromising scale, realism and external validity. With the proposed testbed, this paper sets a stepping stone to emulate, within small laboratory spaces, large sensing areas of interest originated from empirical data and simulation models. Over this sensing map, a swarm of low-cost drones can fly allowing the study of a large spectrum of problems such as energy consumption, charging control, navigation and collision avoidance. The applicability of a decentralized multi-agent collective learning algorithm (EPOS) for UAV swarm intelligence along with the assessment of power consumption measurements provide a proof-of-concept and validate the accuracy of the proposed testbed.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2024 The Authors. This is an author-produced version of a paper subsequently published in Advances in Computational Intelligence Systems. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | distributed sensing; swarm intelligence; optimization; drones; UAVs; autonomous search; testbed; smart city |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 09 Aug 2022 10:33 |
Last Modified: | 22 May 2024 10:09 |
Status: | Published |
Publisher: | Springer Nature |
Series Name: | Advances in Intelligent Systems and Computing |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-031-55568-8_48 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:189757 |
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Filename: UKCI_paper_UAV_Smart_Intelligence-2022.pdf
