An Optimization Method for Accelerating UAV Trajectory Planning

An Optimization Method for Accelerating UAV Trajectory Planning | 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Article An Optimization Method for Accelerating UAV Trajectory Planning Yinghuang Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4144325/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Trajectory planning is a very important task in the flight of UAVs. Through trajectory planning, the autonomous flight of UAVs can be realised and the flight efficiency and safety can be improved. Particle swarm algorithm is a commonly used trajectory planning method, but there are some shortcomings in practical applications, such as easy to fall into local minimal values and other problems. Therefore, previous research on particle swarm trajectory planning has been carried out and some improved algorithms have been proposed. However, there are still some problems with these algorithms, such as excessive computational power and slow convergence speed. To address these problems, this paper proposes a method for deep residual learning to optimise the problem of local minima and slow convergence of traditional particle swarm trajectory planning. Specifically, a complete mathematical model for UAV path planning is established in this paper, and path planning results are obtained using the conventional improved particle swarm algorithm by means of cubic spline interpolation. Thereafter, to address the problems that path planning based on the particle swarm optimisation algorithm tends to reach local optimality at a later stage and the slow convergence speed of the improved algorithm, this paper introduces a deep residual learning network to improve the convergence speed and stability of the particle swarm algorithm. Finally, the performance of the algorithm before and after the improvement is compared according to four benchmark test functions. The simulation results show that the improved particle swarm algorithm has a significant enhancement in the late search capability. UAV trajectory planning deep residual learning optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 24 Mar, 2024 Submission checks completed at journal 22 Mar, 2024 First submitted to journal 21 Mar, 2024 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-4144325","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":283282922,"identity":"a143d6cb-9fbe-4f96-8aba-b50dbcc1f94f","order_by":0,"name":"Yinghuang Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYBADGQb2xsYHH4hRygOneQ43G84gTYtEeps0BzFa7Nl7jz0ubLPj4Z/5sEGagcFOTreBkC0859KNZ7Yl80jcTmwwLmBINjY7QEiLRI6ZNG8bMw8DUEvyDIYDiduI1FLPI3/zYMNhHhK0HOYxuMHY2EycljNnzKR5zh3nMTyT2Mw4w4AIv7C39wC1lFXLyR0//vzHhwo7OYJawICRDcYyIEY5GPwhWuUoGAWjYBSMRAAAaOk7nDl9/54AAAAASUVORK5CYII=","orcid":"","institution":"Nanchang Hangkong University","correspondingAuthor":true,"prefix":"","firstName":"Yinghuang","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-03-21 14:44:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4144325/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4144325/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53457745,"identity":"34c7ae69-54d5-4ca7-87db-380cce102a80","added_by":"auto","created_at":"2024-03-26 08:24:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1578724,"visible":true,"origin":"","legend":"","description":"","filename":"UAVTrajectoryPlanning.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4144325/v1_covered_e65cc590-942b-42a5-ac17-4d26ad0c3311.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Optimization Method for Accelerating UAV Trajectory Planning","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":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"UAV, trajectory planning, deep residual learning, optimization","lastPublishedDoi":"10.21203/rs.3.rs-4144325/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4144325/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTrajectory planning is a very important task in the flight of UAVs. 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