Research on safe path planning for unmanned aerial vehicles based on an Improved Pied Kingfisher Optimization Algorithm

Research on safe path planning for unmanned aerial vehicles based on an Improved Pied Kingfisher Optimization Algorithm | 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 Research on safe path planning for unmanned aerial vehicles based on an Improved Pied Kingfisher Optimization Algorithm Chengyu Liu, Shixin Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5739312/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 safety route planning problem in UAVs (Unmanned Aerial Vehicles)missions is the subject of this paper, which suggests a method based on an improved Pied Kingfisher Optimization algorithm (IPKO) algorithm. By adding collision avoidance restrictions to the swarm scenario, the study expands the single UAVs path planning problem to improve flight safety. In order to reduce the problem of decreased convergence speed in the PKO algorithm, population diversity is increased and premature convergence to the local optimum is prevented by using mirror reflection learning. Furthermore, the algorithm is improved with a crash mechanism model and a fish hawk hunting approach, which allows UAVs to react in real time to environmental changes and avoid hazards. The IPKO algorithm performs better than conventional algorithms in terms of path efficiency and safety, as evidenced by experimental results, which also provide a fresh view of UAV safety path planning. Physical sciences/Engineering Physical sciences/Mathematics and computing Collision avoidance path planning pied kingfisher optimization algorithm (PKO) unmanned aerial vehicles (UAVs) 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-5739312","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":400222825,"identity":"1f11633d-da54-4ed0-af74-c1cc7ed871fd","order_by":0,"name":"Chengyu Liu","email":"","orcid":"","institution":"Jiangsu University of science and technology","correspondingAuthor":false,"prefix":"","firstName":"Chengyu","middleName":"","lastName":"Liu","suffix":""},{"id":400222826,"identity":"75ad5ea8-fb87-4951-a472-e83a9b35936d","order_by":1,"name":"Shixin Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACNmb+7x8+VNhAeDzEaOFnbzBjnHEmjQQtkj0HzJh52w6ToMXgRkLaY54z5xPnz0hgfPC2jUHenAgtxw3nVNxObJyRwGw4t43BcGcDQS2JDRJvztxObJZIYJPmbWNIMDhAUEsygwRv27nENokE9t9EaZHsOcYmydt2ILEHaAszUVr42XuYDWecSTaewfOwWXLOOQnDDYS0sDHzMD74UGEnO789+eCHN2U28gRtgQHHBgbGBiAtQaR6ILAnXukoGAWjYBSMOAAA3mdCzhlPFV0AAAAASUVORK5CYII=","orcid":"","institution":"Jiangsu University of science and technology","correspondingAuthor":true,"prefix":"","firstName":"Shixin","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2024-12-31 05:23:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5739312/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5739312/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102745470,"identity":"a62be898-1ee1-4ee8-b303-34dcc548fc58","added_by":"auto","created_at":"2026-02-16 08:50:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1455656,"visible":true,"origin":"","legend":"","description":"","filename":"2025.1.9.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5739312/v1_covered_8fde4e4b-3623-4b03-8213-3fb809de6ac0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on safe path planning for unmanned aerial vehicles based on an Improved Pied Kingfisher Optimization Algorithm","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":"Collision avoidance, path planning, pied kingfisher optimization algorithm (PKO), unmanned aerial vehicles (UAVs)","lastPublishedDoi":"10.21203/rs.3.rs-5739312/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5739312/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe safety route planning problem in UAVs (Unmanned Aerial Vehicles)missions is the subject of this paper, which suggests a method based on an improved Pied Kingfisher Optimization algorithm (IPKO) algorithm. 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