{"paper_id":"00847430-e59a-480d-b9ef-d17c57088ec5","body_text":"Path planning and trajectory optimization based on an improved RRT 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 Research Article Path planning and trajectory optimization based on an improved RRT algorithm Chaofan Teng, Luping Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4320725/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 study presents an enhanced motion planning and trajectory optimization algorithm for Rapidly-exploring Random Trees (RRT), addressing the issues of excessive randomness, unnecessary path segments, and discontinuities in the trajectory that are evident in conventional RRT path planning algorithms. Initially, a dynamic sampling strategy was developed to minimize the search's randomness by managing the generation location of random points. Subsequently, the artificial potential field method was incorporated into the RRT algorithm to enable nodes to account for the influence of obstacles and target points during expansion; thus, mitigating the search's aimlessness. Redundant sections were then eliminated, and the path distance was reduced, all the while maintaining a safe distance from obstacles. Finally, by employing the minimum snap method in conjunction with flight corridors and time reallocation, the trajectory was rendered smooth and continuous, ensuring that physical quantities remained within acceptable limits without encountering obstacles. The comparative simulation results demonstrate that the proposed algorithm markedly decreases the number of nodes extended during the path search and reduces the search path length in comparison to the traditional RRT. Path planning RRT Minimum snap Trajectory optimization Time reallocation 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-4320725\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":300728891,\"identity\":\"c2e8e7f5-f312-489a-9a35-60672be55135\",\"order_by\":0,\"name\":\"Chaofan Teng\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBACfvnHx398qLCxY2w4fIA4LZINaQmSM86kJTM3HksgTovBgRwDac62Q4ztzWcMiHTZgQMGxgxsB5h52858vPGGwU5Ot4GADsbGhoTkAp47fJI9ZzdbzmFINjY7QEALMzPDgcMzJJ4xG844u02ah+FA4jZCWtjYGBubeQwOM+6//+YZcVp4eJiZmXkSDgMdeIaNOC0SEkB7ZhxIS2ZsOGZsOceACL/Y3+D/xvDxHzgqH954U2EnR1ALqpU8xEYNkhZSdYyCUTAKRsGIAABCt0flVUUN9AAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Shenyang Aerospace University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Chaofan\",\"middleName\":\"\",\"lastName\":\"Teng\",\"suffix\":\"\"},{\"id\":300728895,\"identity\":\"e33d0e2b-aa2b-4e79-9f7b-49dbaa9d7ea7\",\"order_by\":1,\"name\":\"Luping Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shenyang Aerospace University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Luping\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-04-25 01:13:01\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4320725/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4320725/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":63125299,\"identity\":\"a280291f-e8ee-4b39-8f5d-13731a23e697\",\"added_by\":\"auto\",\"created_at\":\"2024-08-23 12:07:33\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1334078,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"PathplanningandtrajectoryoptimizationbasedonanimprovedRRTalgorithm.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4320725/v1_covered_13b47848-1f26-4bab-b368-f51e86bfbf7b.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Path planning and trajectory optimization based on an improved RRT 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\":\"info@researchsquare.com\",\"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\":\"Path planning, RRT, Minimum snap, Trajectory optimization, Time reallocation\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4320725/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4320725/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThis study presents an enhanced motion planning and trajectory optimization algorithm for Rapidly-exploring Random Trees (RRT), addressing the issues of excessive randomness, unnecessary path segments, and discontinuities in the trajectory that are evident in conventional RRT path planning algorithms. 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