Multi-Task Agent Hybrid Control in Sparse Maps and Complex Environmental Conditions

Multi-Task Agent Hybrid Control in Sparse Maps and Complex Environmental Conditions | 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 Multi-Task Agent Hybrid Control in Sparse Maps and Complex Environmental Conditions Linhai Wang, Su Yu, Mou Li, Xiaolong Wei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4255412/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 With the rapid development of space exploration technology, the detection of extraterrestrial bodies has become increasingly important. Among these, path planning and target recognition and positioning technologies are particularly critical for applications in intelligent agents with low computational power operating in complex environments. This paper introduces an A* path planning algorithm with an adaptive heuristic function, which demonstrates improved robustness in low-resolution maps and can plan paths that stay as far away from obstacles as possible, even when the accuracy of the prior map is limited. Additionally, this study proposes a Dynamic Environment Target Identification and Localization (DETIL) algorithm, which includes the identification of unknown obstacles and the spatio-temporal dimension clustering to locate points of interest. Simulation results of the mixed control scheme using both algorithms indicate that the improved A* algorithm reduces the maximum elevation difference by 55% and the maximum cumulative elevation difference by 68% compared to the traditional A* algorithm. The unknown obstacle identification component of the DETIL algorithm can recognize all obstacles along the path, while the spatio-temporal dimension clustering section improves the average number of target discoveries by 152% over the conventional DBSCAN clustering approach. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Software Extreme Environments Multi-Task Intelligent Agents Path Planning Obstacle Recognition Points of Interest Localization 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. 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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-4255412","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":292588807,"identity":"a8937045-dfca-4679-bc17-8a888eca9e33","order_by":0,"name":"Linhai Wang","email":"","orcid":"","institution":"Shanghai University Of Engineering Science","correspondingAuthor":false,"prefix":"","firstName":"Linhai","middleName":"","lastName":"Wang","suffix":""},{"id":292588808,"identity":"62bd98e7-a1a4-4bca-9624-11c93419313c","order_by":1,"name":"Su Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYLCCD1BagmgdjDNI1sLMQ5IWgxvpz6Rt/hyWk29gPnibh8EujwgtOWbSuW2HjQ0OsCVb8zAkFxOjhU06t+Fw4gYGHjNpHoYDiQ1EOcziz+H6+Q3834jVkmAmzcB2OIHhAA8bcVokz7wxtuxtSzfccJjN2HKOQTJhLXzH0x/e+PHHWl6+vfnhjTcVdoS1KByAsZjB7iSkHgjkCRo6CkbBKBgFowAAa6g4af3F5ZgAAAAASUVORK5CYII=","orcid":"","institution":"Shanghai University Of Engineering Science","correspondingAuthor":true,"prefix":"","firstName":"Su","middleName":"","lastName":"Yu","suffix":""},{"id":292588809,"identity":"5930ce88-a165-4286-b0a6-7cbb1034f6c9","order_by":2,"name":"Mou Li","email":"","orcid":"","institution":"Beijing Institute of Control Engineering","correspondingAuthor":false,"prefix":"","firstName":"Mou","middleName":"","lastName":"Li","suffix":""},{"id":292588810,"identity":"57a2d50d-3c84-43ca-95b1-1b0c2797ee33","order_by":3,"name":"Xiaolong Wei","email":"","orcid":"","institution":"Shanghai University Of Engineering Science","correspondingAuthor":false,"prefix":"","firstName":"Xiaolong","middleName":"","lastName":"Wei","suffix":""}],"badges":[],"createdAt":"2024-04-12 05:07:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4255412/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4255412/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66213241,"identity":"40128b45-a6e5-461e-8df6-329827d504d6","added_by":"auto","created_at":"2024-10-08 19:01:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1243673,"visible":true,"origin":"","legend":"","description":"","filename":"MultiTaskAgentHybridControlinSparseMapsandComplexEnvironmentalConditions.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4255412/v1_covered_056c368c-6b9b-49c2-abe3-b6016f5893c5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-Task Agent Hybrid Control in Sparse Maps and Complex Environmental Conditions","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":" Extreme Environments, Multi-Task Intelligent Agents, Path Planning, Obstacle Recognition, Points of Interest Localization","lastPublishedDoi":"10.21203/rs.3.rs-4255412/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4255412/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"With the rapid development of space exploration technology, the detection of extraterrestrial bodies has become increasingly important. 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