Exploration of ML and DL model’s for optimal autonomous docking of a surface vessel

Exploration of ML and DL model’s for optimal autonomous docking of a surface vessel | 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 Exploration of ML and DL model’s for optimal autonomous docking of a surface vessel Lassaad Zaway, Nesrine Affes, Jalel Ktari, Mohamed Amine Boujelbene, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7078143/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 The maritime industry, essential for international logistics and trade, increasingly requires precise and reliable control systems for surface vessels, especially during critical operations such as docking and maneuvering in tight areas. This research presents a data-driven framework for predicting ship control commands, specifically focusing on the number of turns and rudder angle, by using hybrid models that combine deep learning and ensemble machine learning techniques. CNNs are used for feature extraction, while recurrent architectures like GRU and LSTM are used to handle the time-series data generated by a three-DOF vessel model operating in a simulated port environment. To improve prediction performance, these deep learning models are combined with Random Forest and Extra Trees regressors. Comparative evaluations show that the GRU+ExtraTree model delivers superior accuracy and responsiveness, particularly during sudden changes in control signals, due to its ability to capture temporal dependencies. The proposed method shows promising potential for real-time trajectory tracking and autonomous docking applications in maritime navigation. Autonomous Docking Vessel Machine Learning Deep Learning Real-Time Hybrid model 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-7078143","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":491218528,"identity":"bbf66bf5-48b7-4fe9-b566-e707e6fa029a","order_by":0,"name":"Lassaad Zaway","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYNCCAjBp+ABI8PARp8UATBqDKB42UrSYSYBIglrk288YfuYxsLPnbz+8rfJrjp0MGwPzw0c38Ghh7MkxluYxSE6ccSat7LbstmSgw9iMjXPwaGFmyN0A1MKcwHAgx+y25DZmoBYeNml8Wtj4327+zWNQby9//o1ZseS2esJaeCRytwFtOcy44UaOGePHbYcJa5GQeP/Nco7B8cSNN54VSzNuO87DxkzAL/L9ack33lRU28udT9748ee2ant+9uaHj/FpAQEmHiiDGcxgJqAcBBh/oDNGwSgYBaNgFCADAJAVQiZI2x0ZAAAAAElFTkSuQmCC","orcid":"","institution":"University of Sfax","correspondingAuthor":true,"prefix":"","firstName":"Lassaad","middleName":"","lastName":"Zaway","suffix":""},{"id":491218531,"identity":"916506f2-9250-43a9-9d6b-70e4a388849a","order_by":1,"name":"Nesrine Affes","email":"","orcid":"","institution":"University of Sfax","correspondingAuthor":false,"prefix":"","firstName":"Nesrine","middleName":"","lastName":"Affes","suffix":""},{"id":491218532,"identity":"680f9c92-3ca9-4f69-8db5-a32e6ff1c005","order_by":2,"name":"Jalel Ktari","email":"","orcid":"","institution":"University of Sfax","correspondingAuthor":false,"prefix":"","firstName":"Jalel","middleName":"","lastName":"Ktari","suffix":""},{"id":491218534,"identity":"29b08fef-db19-44b0-a299-c4c8b266a964","order_by":3,"name":"Mohamed Amine Boujelbene","email":"","orcid":"","institution":"University of Sfax","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"Amine","lastName":"Boujelbene","suffix":""},{"id":491218535,"identity":"dacd6459-3f7a-40b4-8b56-99080a7555fb","order_by":4,"name":"Mohamed Abid","email":"","orcid":"","institution":"University of Picardie Jules Verne","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"","lastName":"Abid","suffix":""}],"badges":[],"createdAt":"2025-07-08 21:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7078143/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7078143/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92856237,"identity":"e0f3afee-0239-4a39-83df-5df48b9de215","added_by":"auto","created_at":"2025-10-06 11:32:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2253068,"visible":true,"origin":"","legend":"","description":"","filename":"ExplorationofMLandDLmodelsforoptimalautonomousdockingofasurfacevessel.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7078143/v1_covered_81beb518-2279-4ef0-b641-fa25a3dadb7e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploration of ML and DL model’s for optimal autonomous docking of a surface vessel","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":"Autonomous Docking, Vessel, Machine Learning, Deep Learning, Real-Time, Hybrid model","lastPublishedDoi":"10.21203/rs.3.rs-7078143/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7078143/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The maritime industry, essential for international logistics and trade, increasingly requires precise and reliable control systems for surface vessels, especially during critical operations such as docking and maneuvering in tight areas. 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