Article (Scientific journals)
Learning to Optimise a Swarm of UAVs
Duflo, Gabriel; Danoy, Grégoire; Talbi, El-Ghazali et al.
2022In Applied Sciences, 12 (19 9587)
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Abstract :
[en] The use of Unmanned Aerial Vehicles (UAVs) has shown a drastic increase in interest in the past few years. Current applications mainly depend on single UAV operations, which face critical limitations such as mission range or resilience. Using several autonomous UAVs as a swarm is a promising approach to overcome these. However, designing an efficient swarm is a challenging task, since its global behaviour emerges solely from local decisions and interactions. These properties make classical multirobot design techniques not applicable, while evolutionary swarm robotics is typically limited to a single use case. This work, thus, proposes an automated swarming algorithm design approach, and more precisely, a generative hyper-heuristic relying on multi-objective reinforcement learning, that permits us to obtain not only efficient but also reusable swarming behaviours. Experimental results on a three-objective variant of the Coverage of a Connected UAV Swarm problem demonstrate that it not only permits one to generate swarming heuristics that outperform the state-of-the-art in terms of coverage by a swarm of UAVs but also provides high stability. Indeed, it is empirically demonstrated that the model trained on a certain class of instances generates heuristics and is capable of performing well on instances with a different size or swarm density.
Disciplines :
Computer science
Author, co-author :
Duflo, Gabriel ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > PCOG
Danoy, Grégoire  ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
Talbi, El-Ghazali ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
Bouvry, Pascal ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
External co-authors :
no
Language :
English
Title :
Learning to Optimise a Swarm of UAVs
Publication date :
2022
Journal title :
Applied Sciences
ISSN :
2076-3417
Publisher :
MDPI, Basel, Switzerland
Volume :
12
Issue :
19 9587
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBilu :
since 27 October 2022

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