CRGWO-DWA: A Structured Global–Local Collaborative Planning Framework for Smooth UAV Navigation in Dynamic Environments

CRGWO-DWA: A Structured Global–Local Collaborative Planning Framework for Smooth UAV Navigation in Dynamic 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 CRGWO-DWA: A Structured Global–Local Collaborative Planning Framework for Smooth UAV Navigation in Dynamic Environments Yong He, Qiang Gao, Qing Huang, Boji Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9188935/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract UAV path planning in dynamic environments requires balancing global path quality, local obstacle avoidance, and motion smoothness. To address the premature convergence and unsmooth paths of the Grey Wolf Optimizer (GWO) and the local-optimum tendency and evaluation imbalance of the Dynamic Window Approach (DWA), this paper proposes CRGWO-DWA, a structured global-local collaborative planning framework. In the global planning stage, CRGWO enhances population diversity and search performance through piecewise linear chaotic initialization, periodic regeneration of the control parameter, and adaptive leadership weighting, while cubic spline interpolation is used to generate a smooth reference path. In the local planning stage, DWA is improved using min-max normalized evaluation and redesigned weighting to enhance trajectory discrimination and responsiveness under dynamic obstacle interference. Experimental results show that CRGWO achieves the best or tied-best performance in 32 of 36 benchmark metrics, reduces path length and redundant turning nodes in static environments, and produces more compact and smoother paths in three-dimensional scenarios. In dynamic multi-obstacle environments, CRGWO-DWA enables safe and smooth UAV navigation with stable velocity profiles. These results indicate that the proposed framework provides an effective solution for UAV path planning in dynamic environments. Physical sciences/Engineering Physical sciences/Mathematics and computing grey wolf optimizer dynamic window approach unmanned aerial vehicle path planning dynamic obstacle avoidance Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 21 Apr, 2026 Reviews received at journal 19 Apr, 2026 Reviews received at journal 12 Apr, 2026 Reviewers agreed at journal 12 Apr, 2026 Reviews received at journal 11 Apr, 2026 Reviews received at journal 10 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers agreed at journal 31 Mar, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviewers invited by journal 29 Mar, 2026 Editor invited by journal 27 Mar, 2026 Editor assigned by journal 24 Mar, 2026 Submission checks completed at journal 24 Mar, 2026 First submitted to journal 22 Mar, 2026 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. 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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-9188935","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":615011346,"identity":"993de8ac-e05d-4e66-a9bb-672966b1a1d0","order_by":0,"name":"Yong He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBACNmb2w49/VNjA+MyEtfCz96QZM5xJA7EZG4jSItlzwECaseUwCVoMbiQkGBc2nJeXn5H+/AFDhXViA/vZAwS0JB54PHPHbcMNN3IMGxjOpCc28OQlELTFgPfM7QQDiRzGBsa2w4kNEjwGhLQYSPC2nUsAOuxhA+M/IrSAvc/bdiCB4UaCYQNjAxFaQIFsOONMsuGGM28MZyQcSzdu48nBrwUUlQ8+VNjJy7enP/jwocZatp/9DH4tqCABZAgJ6kfBKBgFo2AU4AAAKutKg9/yYKIAAAAASUVORK5CYII=","orcid":"","institution":"Changsha University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Yong","middleName":"","lastName":"He","suffix":""},{"id":615011349,"identity":"f0a242e0-24c7-4ca6-8963-625b4b8bfde5","order_by":1,"name":"Qiang Gao","email":"","orcid":"","institution":"Changsha University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Gao","suffix":""},{"id":615011350,"identity":"6abee1c4-7857-4534-bb16-f8f55ce64431","order_by":2,"name":"Qing Huang","email":"","orcid":"","institution":"Changsha University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Huang","suffix":""},{"id":615011351,"identity":"9193a4c7-d786-4b27-9350-256987e2d39d","order_by":3,"name":"Boji Huang","email":"","orcid":"","institution":"Changsha University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Boji","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2026-03-22 04:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9188935/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9188935/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105905250,"identity":"21c27dd0-4aba-4ec3-94e1-0b42ef3f414a","added_by":"auto","created_at":"2026-04-01 10:11:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1341467,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9188935/v1_covered_8ad55eef-889e-4857-aa4b-a19b7eee0eab.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CRGWO-DWA: A Structured Global–Local Collaborative Planning Framework for Smooth UAV Navigation in Dynamic Environments","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"grey wolf optimizer, dynamic window approach, unmanned aerial vehicle, path planning, dynamic obstacle avoidance","lastPublishedDoi":"10.21203/rs.3.rs-9188935/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9188935/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUAV path planning in dynamic environments requires balancing global path quality, local obstacle avoidance, and motion smoothness. To address the premature convergence and unsmooth paths of the Grey Wolf Optimizer (GWO) and the local-optimum tendency and evaluation imbalance of the Dynamic Window Approach (DWA), this paper proposes CRGWO-DWA, a structured global-local collaborative planning framework. In the global planning stage, CRGWO enhances population diversity and search performance through piecewise linear chaotic initialization, periodic regeneration of the control parameter, and adaptive leadership weighting, while cubic spline interpolation is used to generate a smooth reference path. In the local planning stage, DWA is improved using min-max normalized evaluation and redesigned weighting to enhance trajectory discrimination and responsiveness under dynamic obstacle interference. 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