GNLC-TCM: Integrated Global Navigation and Local Control with a Traffic Capacity Model for UAV Swarms in Constrained Environments

GNLC-TCM: Integrated Global Navigation and Local Control with a Traffic Capacity Model for UAV Swarms in Constrained Environments | 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 Article GNLC-TCM: Integrated Global Navigation and Local Control with a Traffic Capacity Model for UAV Swarms in Constrained Environments Arkadiusz Bożko, Leszek Ambroziak, Ewa Pawłuszewicz, RangaRao Venkatesha Prasad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6531764/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 increasing demand for autonomous Unmanned Aerial Vehicle (UAV) operations in constrained environments such as indoor spaces, industrial facilities, and urban infrastructure has led to a growing interest in swarm-based navigation and coordination strategies. Navigating multiple UAVs through complex, limited-access environments presents significant challenges in collision avoidance, dynamic path planning, and airspace management. This study investigates a hybrid approach to UAV swarm navigation in restricted spaces by integrating global and local control mechanisms. The proposed architecture, termed GNLC-TCM (Global Navigation and Local Control with a Traffic Capacity Model), combines local reactive navigation using the Artificial Potential Field (APF) algorithm with a graph-based global planner. The global planner models the environment as an undirected graph, where nodes represent navigable waypoints, and edge weights are based on an introduced concept of the traffic capacity model. This model accounts for the expected UAV flow through each connection, enabling better distribution of agents and minimizing congestion. A simulation environment was developed using a basic multi-agent configuration within sample environments to evaluate the proposed system. Performance metrics for target reachability rate and adaptability to traffic distribution were observed. Preliminary results demonstrate the feasibility of the dual-layer architecture and its potential for managing UAV swarms in confined spaces. The paper concludes with a discussion on the observed system behaviour and proposes several directions for future research, including dynamic re-weighting strategies and hardware implementation in real UAV platforms. Physical sciences/Energy science and technology Physical sciences/Engineering 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-6531764","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":487486403,"identity":"a96c7e0a-9dba-4d18-864e-2c3c25b4e9dc","order_by":0,"name":"Arkadiusz Bożko","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIie3QMWrDMBSA4WcM8uI4q4IhvoJMIKXU4KtIBOwpoRDorGJwl3YvVL1Dt5LNRuBJZPbY7i54LKRDZUiTyU67haIfhLR8SE8AJtNZhgBc0h1cvQqYAt6fvd+S2YGgQQJHwvgpMn6qWNtcyzjgo/L9U0Xpq8+tt48cVn0EbxM5EUSyW+4tCK6T5UYUdvicw7r3YcrhvksktcGdY9LK5UtNkT/KgeU9IlBOttMkRh2hrUxJTZ3dECEKVd0t1n1HilpSTZA9REKFkktBUvZoe4uQqyTciDKbiC3unWWq0Kxuvq7i4O5B/1gVBRd+VrbNTbQa941/yP55Klhcb5ieFMfp9vsfiMlkMv3zvgE0clWGNGtebQAAAABJRU5ErkJggg==","orcid":"","institution":"Bialystok University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Arkadiusz","middleName":"","lastName":"Bożko","suffix":""},{"id":487486404,"identity":"1356c664-fadb-4077-aa6f-31a6aed181bb","order_by":1,"name":"Leszek Ambroziak","email":"","orcid":"","institution":"Bialystok University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Leszek","middleName":"","lastName":"Ambroziak","suffix":""},{"id":487486405,"identity":"5e434b4c-b37b-42d0-b5f7-66b3f4d383db","order_by":2,"name":"Ewa Pawłuszewicz","email":"","orcid":"","institution":"Bialystok University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Ewa","middleName":"","lastName":"Pawłuszewicz","suffix":""},{"id":487486406,"identity":"71c9ef30-8bea-4029-a85e-f23292893ab3","order_by":3,"name":"RangaRao Venkatesha Prasad","email":"","orcid":"","institution":"Delft University of Technology","correspondingAuthor":false,"prefix":"","firstName":"RangaRao","middleName":"Venkatesha","lastName":"Prasad","suffix":""}],"badges":[],"createdAt":"2025-04-25 22:38:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6531764/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6531764/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93054909,"identity":"2aaf5d4d-fe6d-4131-afb6-b99ee9798adb","added_by":"auto","created_at":"2025-10-08 14:46:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1134630,"visible":true,"origin":"","legend":"","description":"","filename":"fdkbqsdmmmhqwypfpknnkfjwxvnhnrrs.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6531764/v1_covered_ff1815a0-47f6-4633-8433-debea0da9fba.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"GNLC-TCM: Integrated Global Navigation and Local Control with a Traffic Capacity Model for UAV Swarms in Constrained Environments","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":"","lastPublishedDoi":"10.21203/rs.3.rs-6531764/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6531764/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The increasing demand for autonomous Unmanned Aerial Vehicle (UAV) operations in constrained environments such as indoor spaces, industrial facilities, and urban infrastructure has led to a growing interest in swarm-based navigation and coordination strategies. Navigating multiple UAVs through complex, limited-access environments presents significant challenges in collision avoidance, dynamic path planning, and airspace management. This study investigates a hybrid approach to UAV swarm navigation in restricted spaces by integrating global and local control mechanisms. The proposed architecture, termed GNLC-TCM (Global Navigation and Local Control with a Traffic Capacity Model), combines local reactive navigation using the Artificial Potential Field (APF) algorithm with a graph-based global planner. The global planner models the environment as an undirected graph, where nodes represent navigable waypoints, and edge weights are based on an introduced concept of the traffic capacity model. This model accounts for the expected UAV flow through each connection, enabling better distribution of agents and minimizing congestion. A simulation environment was developed using a basic multi-agent configuration within sample environments to evaluate the proposed system. Performance metrics for target reachability rate and adaptability to traffic distribution were observed. Preliminary results demonstrate the feasibility of the dual-layer architecture and its potential for managing UAV swarms in confined spaces. 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