Time-dependent Risk-aware Distributed Multi-Agent Path Finder based on A*

Time-dependent Risk-aware Distributed Multi-Agent Path Finder based on A* | 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 Time-dependent Risk-aware Distributed Multi-Agent Path Finder based on A* Samuel Nordström, Yifan Bai, Björn Lindqvist, George Nikolakopoulos This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9266655/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Multi-Agent Path-Finding (MAPF) focuses on the collaborative planning of paths for multiple agents within shared spaces, aiming for collision-free navigation. Conventional planning methods often overlook the presence of other agents, which can cause collisions. In response, this article introduces the A*+T algorithm, a distributed approach that improves coordination among agents by anticipating their positions based on their movement speeds. The algorithm also considers dynamic obstacles, assessing potential collisions with respect to observed speeds and trajectories, thereby facilitating collision-free path planning in environments populated by other agents and moving objects. It incorporates a risk layer that surrounds both dynamic and static entities, which enhances its utility in real-world applications. Each agent functions autonomously while being mindful of the paths planned by others, effectively addressing the complexities inherent in multi-agent situations. The performance of A*+T has been rigorously tested in the Gazebo simulation and compared against established approaches such as CBS, ECBS, and SIPP where it showed improved capability of finding traversable paths. Furthermore, the algorithm has shown competence in single-agent experiments, with results demonstrating its effectiveness in managing dynamic obstacles and affirming its practical relevance across various scenarios. Multi-agent path finding Distributed planning Dynamic obstacle avoidance Risk-aware navigation A∗ +T Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 15 May, 2026 Reviewers agreed at journal 11 May, 2026 Reviewers agreed at journal 03 May, 2026 Reviewers invited by journal 22 Apr, 2026 Editor assigned by journal 16 Apr, 2026 Submission checks completed at journal 14 Apr, 2026 First submitted to journal 30 Mar, 2026 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-9266655","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":630138969,"identity":"c0561328-ee7b-4d29-9d71-bdde886b148d","order_by":0,"name":"Samuel Nordström","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYNCCAjBpACb52YnSYgAlDwBJyWZmUrUYHCagRb79dJrEBwOGPH7pw5s/f9xjE218mP+YBMMfG9zmn8ndJjnDgKFYsi+tTOLAs7TcbYeZ2SQY29LwOCl3mzSPAUPihjM8ZgwHDhyGamk4jNth/W+3Sf+BaDH+ANKyuRmoheHPf9yeuQG0hQGixUACpGUDM0gL2wHcDrvxdrNlj4FE4swetjKJMwfScmccZja2SGxLxuOw3I03flTYJPbzMG/+UHHAJre/vfHhjQ9/7HA7DAIk0PgJhDSMglEwCkbBKMALAIsQUs3koNnYAAAAAElFTkSuQmCC","orcid":"","institution":"Luleå University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Samuel","middleName":"","lastName":"Nordström","suffix":""},{"id":630138971,"identity":"6322cecb-983a-4952-8a8c-9b358750757d","order_by":1,"name":"Yifan Bai","email":"","orcid":"","institution":"Luleå University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yifan","middleName":"","lastName":"Bai","suffix":""},{"id":630138972,"identity":"729c58c7-04bb-42b5-80a5-654032e3d810","order_by":2,"name":"Björn Lindqvist","email":"","orcid":"","institution":"Luleå University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Björn","middleName":"","lastName":"Lindqvist","suffix":""},{"id":630138974,"identity":"ba211c6f-5c7e-415f-aab3-f46194bc8e91","order_by":3,"name":"George Nikolakopoulos","email":"","orcid":"","institution":"Luleå University of Technology","correspondingAuthor":false,"prefix":"","firstName":"George","middleName":"","lastName":"Nikolakopoulos","suffix":""}],"badges":[],"createdAt":"2026-03-30 12:10:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9266655/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9266655/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108007084,"identity":"abaa3adb-ffa4-4ec7-9adb-b47c2462eb00","added_by":"auto","created_at":"2026-04-28 12:58:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1638673,"visible":true,"origin":"","legend":"","description":"","filename":"JINTsubmitted03.26asptATimedependentRiskawaredistributedMultiAgentPathFinderbasedonA3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9266655/v1_covered_872365bb-f6ab-4a96-bec3-25c7ee0f160f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Time-dependent Risk-aware Distributed Multi-Agent Path Finder based on A*","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":"journal-of-intelligent-and-robotic-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Intelligent \u0026 Robotic Systems](https://link.springer.com/journal/10846)","snPcode":"10846","submissionUrl":"https://submission.springernature.com/new-submission/10846/3","title":"Journal of Intelligent \u0026 Robotic Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Multi-agent path finding, Distributed planning, Dynamic obstacle avoidance, Risk-aware navigation, A∗ +T ","lastPublishedDoi":"10.21203/rs.3.rs-9266655/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9266655/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMulti-Agent Path-Finding (MAPF) focuses on the collaborative planning of paths for multiple agents within shared spaces, aiming for collision-free navigation. 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