DASA: a direction-aware and self-adaptive A algorithm with learned heuristic for UAV path planning of smart city

DASA: a direction-aware and self-adaptive A algorithm with learned heuristic for UAV path planning of smart city | 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 DASA*: a direction-aware and self-adaptive A* algorithm with learned heuristic for UAV path planning of smart city Xinshi Zhang, Li Tan, Jiaqin Chai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6725799/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Path planning is a fundamental component in the development of robotics, autonomous navigation, and intelligent systems, playing a pivotal role in the functioning of smart cities. Within the realm of smart cities, where infrastructure is becoming increasingly interconnected, efficient path planning algorithms are essential for optimizing traffic flow, reducing congestion, and ensuring the seamless movement of people and goods. Among various path planning algorithms, the A* algorithm remains one of the most widely used approaches due to its completeness and optimality under consistent heuristics. However, traditional A* suffers from several limitations when applied to complex 3D environments, including uniform neighbor expansion, fixed-resolution grids, and overly simplistic heuristic functions. These drawbacks often lead to excessive computation, suboptimal paths, and failure in cluttered or large-scale scenarios. To address these challenges, we propose a direction-aware and self-adaptive A* algorithm named DASA*, an enhanced A*-based path planning framework. First, we introduce a direction-aware neighbor selection mechanism that prioritizes node expansion along vectors aligned with the goal, significantly reducing unnecessary exploration. Second, a resolution-adaptive search strategy dynamically adjusts the planning granularity according to local obstacle density, improving both efficiency and safety in heterogeneous environments. Third, we design a learned heuristic interface that supports the integration of neural models trained on spatial and semantic environmental features, enabling more informed and goal-directed search behavior. Finally, we design a path adjustment strategy to simplify the path by removing unnecessary path points to generate smoother and more natural planning trajectories. Additionally, DASA* features a robust fallback mechanism to guarantee path discovery even when guided strategies are overly restrictive. Subsequently, Extensive experiments in simulated 3D environments demonstrate that DASA* outperforms conventional A* and its variants in terms of planning time, length of optimal path , and success rate. The proposed framework provides a practical and extensible foundation for real-world applications such as UAV navigation, mobile robotics, and autonomous inspection in complex terrains. Physical sciences/Engineering/Aerospace engineering Physical sciences/Mathematics and computing/Computer science Path Planning A* Algorithm Direction-Aware Search Adaptive Resolution Learned Heuristic UAV Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 04 Jul, 2025 Reviews received at journal 04 Jul, 2025 Reviews received at journal 27 Jun, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviewers invited by journal 23 Jun, 2025 Editor assigned by journal 23 Jun, 2025 Editor invited by journal 05 Jun, 2025 Submission checks completed at journal 04 Jun, 2025 First submitted to journal 22 May, 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. 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-6725799","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":475910651,"identity":"001cc53d-bb9c-4957-b1e2-084db12165fa","order_by":0,"name":"Xinshi Zhang","email":"","orcid":"","institution":"Beijing Technology and Business University","correspondingAuthor":false,"prefix":"","firstName":"Xinshi","middleName":"","lastName":"Zhang","suffix":""},{"id":475910652,"identity":"8bfc4f3d-df5c-48bb-9a5e-1513c7cdbd3c","order_by":1,"name":"Li Tan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYHACxgMJFQwJDBIMbGBeAyH1PEB8IOGMAVALMylaGNtI0WLPfvbAgYfz/uQxSPcfe8zDYCO74QDzswd4beHJSziQuM2gmEHmMLsxD0Oa8YYDbOYG+B2WYwDSktggkcwmzcNwOHHDAR42Cbxa+N8AtcyBa/lPhBYJkC0NcC0HiNByA2hLwjHjxDaZw+aGcwySjWceZjPDq4W9P8fw4Y8aucR+6cZnD95U2Mn2HW9+hlcLHIAjhQEUVMxEqR8Fo2AUjIJRgA8AAItmRUEk4wFPAAAAAElFTkSuQmCC","orcid":"","institution":"Beijing Technology and Business University","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Tan","suffix":""},{"id":475910653,"identity":"ad30b974-2ee6-4fc0-863a-758d8cb4864f","order_by":2,"name":"Jiaqin Chai","email":"","orcid":"","institution":"Beijing Technology and Business University","correspondingAuthor":false,"prefix":"","firstName":"Jiaqin","middleName":"","lastName":"Chai","suffix":""}],"badges":[],"createdAt":"2025-05-22 14:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6725799/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6725799/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-36066-4","type":"published","date":"2026-01-24T15:57:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":101151745,"identity":"44298d0a-2797-4deb-aeeb-032be66f3376","added_by":"auto","created_at":"2026-01-26 16:04:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1189620,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6725799/v1_covered_856c5ec9-51e2-4da7-8e65-0b498ea555ab.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"DASA*: a direction-aware and self-adaptive A* algorithm with learned heuristic for UAV path planning of smart city","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Path Planning, A* Algorithm, Direction-Aware Search, Adaptive Resolution, Learned Heuristic, UAV","lastPublishedDoi":"10.21203/rs.3.rs-6725799/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6725799/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Path planning is a fundamental component in the development of robotics, autonomous navigation, and intelligent systems, playing a pivotal role in the functioning of smart cities. Within the realm of smart cities, where infrastructure is becoming increasingly interconnected, efficient path planning algorithms are essential for optimizing traffic flow, reducing congestion, and ensuring the seamless movement of people and goods. Among various path planning algorithms, the A* algorithm remains one of the most widely used approaches due to its completeness and optimality under consistent heuristics. However, traditional A* suffers from several limitations when applied to complex 3D environments, including uniform neighbor expansion, fixed-resolution grids, and overly simplistic heuristic functions. These drawbacks often lead to excessive computation, suboptimal paths, and failure in cluttered or large-scale scenarios. To address these challenges, we propose a direction-aware and self-adaptive A* algorithm named DASA*, an enhanced A*-based path planning framework. First, we introduce a direction-aware neighbor selection mechanism that prioritizes node expansion along vectors aligned with the goal, significantly reducing unnecessary exploration. Second, a resolution-adaptive search strategy dynamically adjusts the planning granularity according to local obstacle density, improving both efficiency and safety in heterogeneous environments. Third, we design a learned heuristic interface that supports the integration of neural models trained on spatial and semantic environmental features, enabling more informed and goal-directed search behavior. Finally, we design a path adjustment strategy to simplify the path by removing unnecessary path points to generate smoother and more natural planning trajectories. Additionally, DASA* features a robust fallback mechanism to guarantee path discovery even when guided strategies are overly restrictive. Subsequently, Extensive experiments in simulated 3D environments demonstrate that DASA* outperforms conventional A* and its variants in terms of planning time, length of optimal path , and success rate. The proposed framework provides a practical and extensible foundation for real-world applications such as UAV navigation, mobile robotics, and autonomous inspection in complex terrains. 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