Research on geomagnetic perceiving navigation method based on deep reinforcement learning

Research on geomagnetic perceiving navigation method based on deep reinforcement learning | 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 Research on geomagnetic perceiving navigation method based on deep reinforcement learning Li Hong, Xu yan, Liu yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5113044/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 Path planning and navigation of Autonomous Underwater Vehicles face great challenges in unknown environments without a prior information. In this paper, a deep Q-network-based geomagnetic sensing navigation method is proposed to solve the navigation problem without a prior maps. By constructing a deep Q-network model, the method utilizes the powerful representation capability of deep learning to handle complex geomagnetic sensing data. Meanwhile, an action selection strategy combining heuristic and greedy search is introduced to balance exploration and exploitation and enhance the autonomous navigation capability of underwater vehicles in unknown environments. The strategy dynamically adjusts the exploration probability based on the distance to the target, ensuring effective exploration at an early stage and efficient utilization of the learned knowledge when approaching the target. Moreover, the Autonomous Underwater Vehicle explores the environment using local geomagnetic data and trains a regression model to predict the global geomagnetic map. Simulation results show that the method is significantly effective in reducing path lengths, improving exploration efficiency, and enhancing geomagnetic map accuracy. The results show that the method significantly improves the robot's navigation performance in unknown environments and provides a new way to construct geomagnetic maps. 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. 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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-5113044","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":366315896,"identity":"bfbb21bf-f5f8-46ba-8a32-2bfa493b9b2e","order_by":0,"name":"Li Hong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBACPmYeEGUD5bIRoYUNoiUNiJmJ1cIA1nKYFC3svAcfF/w6n7jhdv8Bhg9lhxn4ZzcQchhfsvHMvtvGBncOMzDOOHeYQeLOAUJaeMykeXtuyxncSGZg5m07zGAgkUCUlnM8YC1/idbC8+MAxBZGIrUYG/M2JBtL3jlscLDnXDqPxA0CWvj5zxg+5vljl9h3u/Hhgx9l1nL8MwhoAQPGNiAhwcBwAEjxEKEeBP5AtIyCUTAKRsEowAoAEFE6GEYqAwUAAAAASUVORK5CYII=","orcid":"","institution":"Xi’an University of Posts and Telecommunications","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Hong","suffix":""},{"id":366315897,"identity":"c61f3eca-c0ce-4817-ad06-2cdc39a2340f","order_by":1,"name":"Xu yan","email":"","orcid":"","institution":"Xi’an University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Xu","middleName":"","lastName":"yan","suffix":""},{"id":366315898,"identity":"6f067fa7-d1f4-4df5-a0b5-8c0187b9f22a","order_by":2,"name":"Liu yu","email":"","orcid":"","institution":"Xi’an University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Liu","middleName":"","lastName":"yu","suffix":""}],"badges":[],"createdAt":"2024-09-19 01:48:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5113044/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5113044/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72683346,"identity":"a69fb683-2127-4172-9d36-08003a25f509","added_by":"auto","created_at":"2024-12-31 07:39:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2001837,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5113044/v1_covered_14eba466-5360-469d-9835-e0c9ae74b29d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on geomagnetic perceiving navigation method based on deep reinforcement learning","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":"[email protected]","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":"","lastPublishedDoi":"10.21203/rs.3.rs-5113044/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5113044/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Path planning and navigation of Autonomous Underwater Vehicles face great challenges in unknown environments without a prior information. 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