Reference : Automating the Design of Efficient Distributed Behaviours for a Swarm of UAVs
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/46504
Automating the Design of Efficient Distributed Behaviours for a Swarm of UAVs
English
Duflo, Gabriel mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > PCOG >]
Danoy, Grégoire mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Talbi, El-Ghazali mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Bouvry, Pascal mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Dec-2020
IEEE Symposium Series on Computational Intelligence, Canberra 1-4 December 2020
IEEE
489-496
Yes
International
2020 IEEE Symposium Series on Computational Intelligence (SSCI)
from 01-12-2020 to 04-12-2020
Canberra
Australia
[en] hyper-heuristic ; Q-learning ; UAV swarming ; distributed algorithms
[en] The usage of Unmanned Aerial Vehicles (UAVs) is gradually gaining momentum for commercial applications. The vast majority considers a single UAV, which comes with several constraints such as its range of operations or the number of sensors it can carry. Using multiple autonomous UAVs simultaneously as a swarm makes it possible to overcome these limitations. However, manually designing complex emerging behaviours like swarming is a difficult and tedious task especially for such distributed systems which performance is hardly predictable. This article therefore proposes to automate the design of UAV swarming behaviours by defining a multi-objective optimisation problem, so called Coverage of a Connected-UAV Swarm (CCUS), and designing a Q-Learning based Hyper-Heuristic (QLHH) for generating distributed CCUS heuristics. Experimental results demonstrate the capacity of QLHH to generate efficient heuristics for any instance from a given class.
http://hdl.handle.net/10993/46504
10.1109/SSCI47803.2020.9308355

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