Learning to Optimise a Swarm of UAVs

Learning to Optimise a Swarm of UAVs | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Learning to Optimise a Swarm of UAVs Gabriel Duflo, Grégoire Danoy, El-Ghazali Talbi, Pascal Bouvry This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1854988/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The usage of Unmanned Aerial Vehicles (UAVs) has shown a drastic increase of interest in the past few years. UAVs find applications where human action would be ineffective, slow, risky or even impossible. With a three-dimensional mobility and payload flexibility, they can indeed be used for missions like infrastructure inspection or search and rescue. Most applications have considered the usage of a single UAV so far, but using several autonomous UAVs as a swarm would overcome some drawbacks like mission duration (if one UAV of the swarm is out of battery, the latter can pause the mission while others keep flying) or payload capacity (the payload can be distributed among all UAVs). Designing an efficient swarm of UAVs however comes with some challenges, including the difficulty to define the necessary distributed algorithms to tackle specific tasks. The desired global behaviour, e.g. monitoring an area or transporting material, is indeed emergent from local interactions. Manually designing these local interactions can therefore be tedious and time-consuming. This work thus aims at automating that process in the context of area coverage. The first step has been to define a multi-objective optimisation problem to represent an area coverage mission. The second step has been to define an algorithm to generate distributed heuristic for the latter optimisation problem. The proposed method is based on Q-learning and experimental results demonstrate that it permits to generate heuristics that not only outperform the state-of-the-art, but also provide a high stability. Learning to optimise Hyper-heuristic Multi-objective reinforcement learning UAV swarming distributed algorithm Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1854988","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":121300833,"identity":"afa1bd06-18ae-43fe-bd0a-39b98dfd2f56","order_by":0,"name":"Gabriel Duflo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIie3NIQvCQBjG8ecYmLbls7ivMLEI2v0aGxcsOoPFYDgQZrQqiH6L5RsHs8y+OLGKzGYSd4jYtkXD/cvY8f54AJ3uLzMBD31AkIvAAmjB4E0ILYnhCqSKkAYEX0JC9VNDnNE5KfIFhZ0xiMdBBjZISKtINw3Y1ksp2iWJd5Gct2oJn/TglzduNhPSiqQf1pLNrSQvRRiktW9AHKpW+JfwBsSldwYvoWY7vSLeJmM/NMiqX7mymUryXA479omRvFgO/ON6FWeVK+LzNX9PRtW9WuE1BzqdTqfDGwFhSkxBbpnHAAAAAElFTkSuQmCC","orcid":"","institution":"SnT, University of Luxembourg","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Gabriel","middleName":"","lastName":"Duflo","suffix":""},{"id":121300834,"identity":"8096a2ae-4dcf-4530-be14-910910c88dce","order_by":1,"name":"Grégoire Danoy","email":"","orcid":"","institution":"FSTM/DCS, University of Luxembourg","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Grégoire","middleName":"","lastName":"Danoy","suffix":""},{"id":121300835,"identity":"da4c80c1-7c12-4d5a-b9a7-46158fe760d7","order_by":2,"name":"El-Ghazali Talbi","email":"","orcid":"","institution":"University of Lille, CNRS/CRIStAL, Inria Lille","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"El-Ghazali","middleName":"","lastName":"Talbi","suffix":""},{"id":121300836,"identity":"a26c0c49-e152-4031-a580-389ddf663581","order_by":3,"name":"Pascal Bouvry","email":"","orcid":"","institution":"FSTM/DCS, University of Luxembourg","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Pascal","middleName":"","lastName":"Bouvry","suffix":""}],"badges":[],"createdAt":"2022-07-13 15:59:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1854988/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1854988/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":24058022,"identity":"4fb85246-70bf-4d75-9fb8-880b9cbe0053","added_by":"auto","created_at":"2022-07-19 19:35:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1574916,"visible":true,"origin":"","legend":"","description":"","filename":"SwarmIntelligenceSubmission.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1854988/v1_covered.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Learning to Optimise a Swarm of UAVs","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1854988/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Learning to optimise, Hyper-heuristic, Multi-objective reinforcement learning, UAV swarming, distributed algorithm","lastPublishedDoi":"10.21203/rs.3.rs-1854988/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1854988/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\nThe usage of Unmanned Aerial Vehicles (UAVs) has shown a drastic increase of interest in the past few years. 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