LLM-DWA: A Hybrid Path Planning FrameworkCombining Large Language Models with the Dynamic Window Approach

LLM-DWA: A Hybrid Path Planning FrameworkCombining Large Language Models with the Dynamic Window Approach | 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 LLM-DWA: A Hybrid Path Planning FrameworkCombining Large Language Models with the Dynamic Window Approach Jeonghee Seo, Eunsung Kim, Andrew Jaeyong Choi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7441325/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract This research addresses the local minima problem in the Dynamic Window Approach (DWA) algorithm. The conventionalDWA, which does not incorporate prior environmental knowledge, often shows degraded goal-reaching performance incomplex scenarios, such as environments with U-shaped obstacles, and even when it reaches the goal, the path planningtime is relatively long. To overcome this limitation, we propose an efficient DWA by using Large Language Models (LLMs).Leveraging the reasoning capabilities of LLMs, prior environmental information is interpreted, and appropriate intermediatewaypoints are generated. Experimental results in both 2D grid environments and 3D simulation platforms demonstrate thatthe proposed LLM-based hybrid method achieves higher efficiency and shorter goal-reaching times in U-shaped obstaclescenarios compared to the conventional DWA. These findings highlight the effectiveness of combining the reasoning capabilitiesof LLMs with DWA to improve navigation performance in complex environments. A video demonstration is available at https://youtu.be/Otn53HS4KC4 . Physical sciences/Engineering Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 19 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 22 Oct, 2025 Reviews received at journal 24 Sep, 2025 Reviewers agreed at journal 18 Sep, 2025 Reviews received at journal 18 Sep, 2025 Reviewers agreed at journal 17 Sep, 2025 Reviewers invited by journal 16 Sep, 2025 Editor assigned by journal 16 Sep, 2025 Editor invited by journal 29 Aug, 2025 Submission checks completed at journal 26 Aug, 2025 First submitted to journal 26 Aug, 2025 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. 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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-7441325","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":518494992,"identity":"f58999b9-edc2-4434-aede-6833a0308579","order_by":0,"name":"Jeonghee Seo","email":"","orcid":"","institution":"Gachon University","correspondingAuthor":false,"prefix":"","firstName":"Jeonghee","middleName":"","lastName":"Seo","suffix":""},{"id":518494998,"identity":"70dcf8c2-161e-448b-b4de-76324a163047","order_by":1,"name":"Eunsung Kim","email":"","orcid":"","institution":"Gachon 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