Adaptive Navigation Control of a Bionic Robotic Fish in Complex Karman Vortex Street Flow Fields Using an LSTM-DDPG Hybrid Strategy

Adaptive Navigation Control of a Bionic Robotic Fish in Complex Karman Vortex Street Flow Fields Using an LSTM-DDPG Hybrid Strategy | 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 Adaptive Navigation Control of a Bionic Robotic Fish in Complex Karman Vortex Street Flow Fields Using an LSTM-DDPG Hybrid Strategy Changhui Zheng, Peigang Jiao, Honghao Xu, Yiheng Zhang, Jiaxin Shi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9362471/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Bionic robotic fish suffer from poor navigation robustness, high energy consumption, and control lag in unsteady Karman vortex street flow fields. This study proposes a hybrid adaptive navigation strategy combining long short-term memory (LSTM) and deep deterministic policy gradient (DDPG) to achieve autonomous, efficient, and stable motion control. The method constructs a closed-loop framework of local flow field perception, temporal prediction, and continuous flexible control, eliminating dependence on pre-built flow field models. A Karman vortex street simulation platform is developed using the immersed boundary-lattice Boltzmann method (IB-LBM), and a multi-level reward function is designed to balance accuracy, stability, and energy efficiency. Numerical simulations and physical prototype experiments are conducted under Reynolds numbers Re = 500, 800, and 1000, with comparisons to PID, DQN, and MPC. Results show that the LSTM-DDPG strategy significantly improves navigation precision and anti-disturbance ability while reducing energy consumption. The average task completion rate reaches 88.3%, and average energy consumption is 5.2J/m, 55.3% lower than conventional PID control. This method provides a feasible solution for robust and energy-efficient navigation of bionic robotic fish in complex ocean environments. Physical sciences/Engineering Physical sciences/Mathematics and computing Physical sciences/Physics bionic robotic fish complex marine flow fields adaptive navigation deep reinforcement learning LSTM-DDPG Karman vortex street energy efficiency Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 24 Apr, 2026 Reviewers invited by journal 23 Apr, 2026 Editor assigned by journal 23 Apr, 2026 Editor invited by journal 16 Apr, 2026 Submission checks completed at journal 14 Apr, 2026 First submitted to journal 14 Apr, 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-9362471","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":633784415,"identity":"3ff60d61-c1f7-40d3-97eb-b7e9308573a1","order_by":0,"name":"Changhui Zheng","email":"","orcid":"","institution":"Shandong Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Changhui","middleName":"","lastName":"Zheng","suffix":""},{"id":633784416,"identity":"3e405031-c72c-407b-8777-90b286364f2a","order_by":1,"name":"Peigang Jiao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBACxmYwJQFmP0ioqCFNC7PBgzPHSLORTfJhCzNhZcztzA8fF1RY2PXPbr9WkdjAxsDf3p1AwGFsxsYzzkgkz7hzpuxG4g4ZBokzZzcQ0MJgJs3bJpHMcCMn7UbiGTYGA4lcQlrYv0nz/pNIlgdqKUhsYyZGCw/QlgYJO4Mb6ccYiNVSbDzjmESC4Y0cZomEM8d4CPrFsP/4xscFNXX2cjfSH378UVEjx9/eS0BLAzCggXRiAwOPAUiAB69yEJBngGixZ2Bgf0BQ9SgYBaNgFIxMAAAIxEeCwblbWwAAAABJRU5ErkJggg==","orcid":"","institution":"Shandong Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Peigang","middleName":"","lastName":"Jiao","suffix":""},{"id":633784417,"identity":"2e0cfef2-8bdf-46c8-b8c3-0d7d4d523435","order_by":2,"name":"Honghao Xu","email":"","orcid":"","institution":"Shandong Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Honghao","middleName":"","lastName":"Xu","suffix":""},{"id":633784418,"identity":"aea11cf0-be0e-4d9d-b746-ffe8c4247ea3","order_by":3,"name":"Yiheng Zhang","email":"","orcid":"","institution":"Shandong Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Yiheng","middleName":"","lastName":"Zhang","suffix":""},{"id":633784419,"identity":"60b348cb-15b0-4921-83ee-5fdbf9ab7de0","order_by":4,"name":"Jiaxin Shi","email":"","orcid":"","institution":"Shandong Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Jiaxin","middleName":"","lastName":"Shi","suffix":""}],"badges":[],"createdAt":"2026-04-09 03:10:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9362471/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9362471/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108544615,"identity":"cfb40f1d-3190-4229-b838-83b17be7804b","added_by":"auto","created_at":"2026-05-05 19:55:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2082433,"visible":true,"origin":"","legend":"","description":"","filename":"AdaptiveNavigationControlofaBionicRoboticFishinComplexKarmanVortexStreetFlowFieldsUsinganLSTMDDPGHybridStrategy.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9362471/v1_covered_9e01ce80-e75d-44fc-a1b7-330f3f9d429d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Adaptive Navigation Control of a Bionic Robotic Fish in Complex Karman Vortex Street Flow Fields Using an LSTM-DDPG Hybrid Strategy","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":"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":"bionic robotic fish, complex marine flow fields, adaptive navigation, deep reinforcement learning, LSTM-DDPG, Karman vortex street, energy efficiency","lastPublishedDoi":"10.21203/rs.3.rs-9362471/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9362471/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBionic robotic fish suffer from poor navigation robustness, high energy consumption, and control lag in unsteady Karman vortex street flow fields. This study proposes a hybrid adaptive navigation strategy combining long short-term memory (LSTM) and deep deterministic policy gradient (DDPG) to achieve autonomous, efficient, and stable motion control. The method constructs a closed-loop framework of local flow field perception, temporal prediction, and continuous flexible control, eliminating dependence on pre-built flow field models. A Karman vortex street simulation platform is developed using the immersed boundary-lattice Boltzmann method (IB-LBM), and a multi-level reward function is designed to balance accuracy, stability, and energy efficiency. Numerical simulations and physical prototype experiments are conducted under Reynolds numbers Re\u0026thinsp;=\u0026thinsp;500, 800, and 1000, with comparisons to PID, DQN, and MPC. Results show that the LSTM-DDPG strategy significantly improves navigation precision and anti-disturbance ability while reducing energy consumption. 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