Assessing Collision Risk for Autonomous Drone Swarms in Confined Fluids via Lattice Boltzmann Simulation

Assessing Collision Risk for Autonomous Drone Swarms in Confined Fluids via Lattice Boltzmann Simulation | 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 Assessing Collision Risk for Autonomous Drone Swarms in Confined Fluids via Lattice Boltzmann Simulation Fabio Suim Chagas, Marlon Michael López-Flores, Paulo Fernando Ferreira Rosa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8244854/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 This paper presents a simulation framework for assessing collision risk in autonomous drone swarms operating in confined gaseous environments. The framework couples a three-dimensional 19-velocity (D3Q19) Lattice Boltzmann Method (LBM) fluid solver with explicit rigid-body models of self-propelled drones, using a single-relaxation-time Bhatnagar–Gross–Krook (BGK) operator to enable two-way momentum exchange between drones and fluid and to model inter-drone contacts as perfectly elastic. We simulate swarms of 30 to 60 drones in cubic grids with periodic boundary conditions. The grid sizes reached \((130^3)\) cells, and we tested at different fluid densities ( \((\rho = 1.0, 1.8, 2.93)\) , and \((4.55)\) ). For each setup, we ran multiple simulations as part of an ensemble. We analyzed the outcomes using nonparametric statistical tools—specifically Spearman’s rank correlation \(( r_s)\) , Kendall’s \((\tau)\) , and associated confidence intervals—to understand how often collisions occurred. The results show a strongly nonlinear increase in collision frequency with swarm size, while fluid density has only a minor effect within the tested range. When we increased the domain size from \((100^3)\) to \((130^3)\) , the total number of collisions decreased. However, collision frequency still showed a convex relationship with swarm size. In our simulations, the drones lack built-in collision-avoidance or traffic-coordination mechanisms. As a result, the collision statistics reported here likely represent a conservative upper bound on the collision frequency at a given density, assuming no behavioral safeguards are in place. The framework thus provides a physics-based tool for studying fluid–swarm interaction and for benchmarking collision-avoidance strategies for autonomous multi-robot systems in confined fluids. Aerial vehicles Collision avoidance Swarm dynamics Lattice Boltzmann Methods 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8244854","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":559286778,"identity":"343660a3-9617-4bc6-91c6-ceb97d7cb6de","order_by":0,"name":"Fabio Suim 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Simulation","fulltext":[],"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":true,"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":"Aerial vehicles, Collision avoidance, Swarm dynamics, Lattice Boltzmann Methods","lastPublishedDoi":"10.21203/rs.3.rs-8244854/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8244854/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper presents a simulation framework for assessing collision risk in autonomous drone swarms operating in confined gaseous environments. The framework couples a three-dimensional 19-velocity (D3Q19) Lattice Boltzmann Method (LBM) fluid solver with explicit rigid-body models of self-propelled drones, using a single-relaxation-time Bhatnagar\u0026ndash;Gross\u0026ndash;Krook (BGK) operator to enable two-way momentum exchange between drones and fluid and to model inter-drone contacts as perfectly elastic. We simulate swarms of 30 to 60 drones in cubic grids with periodic boundary conditions. The grid sizes reached \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((130^3)\\)\u003c/span\u003e\u003c/span\u003e cells, and we tested at different fluid densities (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((\\rho = 1.0, 1.8, 2.93)\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((4.55)\\)\u003c/span\u003e\u003c/span\u003e). For each setup, we ran multiple simulations as part of an ensemble. We analyzed the outcomes using nonparametric statistical tools\u0026mdash;specifically Spearman\u0026rsquo;s rank correlation \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(( r_s)\\)\u003c/span\u003e\u003c/span\u003e, Kendall\u0026rsquo;s \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((\\tau)\\)\u003c/span\u003e\u003c/span\u003e, and associated confidence intervals\u0026mdash;to understand how often collisions occurred. The results show a strongly nonlinear increase in collision frequency with swarm size, while fluid density has only a minor effect within the tested range. When we increased the domain size from \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((100^3)\\)\u003c/span\u003e\u003c/span\u003e to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((130^3)\\)\u003c/span\u003e\u003c/span\u003e, the total number of collisions decreased. However, collision frequency still showed a convex relationship with swarm size. In our simulations, the drones lack built-in collision-avoidance or traffic-coordination mechanisms. As a result, the collision statistics reported here likely represent a conservative upper bound on the collision frequency at a given density, assuming no behavioral safeguards are in place. The framework thus provides a physics-based tool for studying fluid\u0026ndash;swarm interaction and for benchmarking collision-avoidance strategies for autonomous multi-robot systems in confined fluids.\u003c/p\u003e","manuscriptTitle":"Assessing Collision Risk for Autonomous Drone Swarms in Confined Fluids via Lattice Boltzmann Simulation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-15 07:02:02","doi":"10.21203/rs.3.rs-8244854/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"15210d3e-a3cc-4081-8744-f3f9fe2417f5","owner":[],"postedDate":"December 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-17T05:41:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-15 07:02:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8244854","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8244854","identity":"rs-8244854","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}