Actor-critic based on Attention Model for Multi-robotCollaborative Backend Optimization

Actor-critic based on Attention Model for Multi-robotCollaborative Backend Optimization | 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 Actor-critic based on Attention Model for Multi-robotCollaborative Backend Optimization Zhaoyi Pei, Zhenghong Yu, Dunlu Lu, Qingqing Bian, Haijie Feng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7797205/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 Backend optimization is an essential component of simultaneous localization and mapping (SLAM).Collaborative backend optimization in multi-robot systems refers to the process of extending single-robot collaboration optimization to coordinate and optimize the backend processes of multiple robots working together, enhancing overall system performance and efficiency.In this paper, a deep reinforcement learning model based on attention mechanisms called MAS-AA specifically tailored for collaborative backend optimization in heterogeneous multi-robot systems is proposed, solved the problem of heterogeneous multi robot system collaborative backend optimization not being able to optimize the selection of map points and pose nodes based on constraints between map points and pose nodes considering robot states and attributes. Firstly, we introduce a collaborative attention neural network designed for multi-robot back-end optimization, along with a collaborative decision-making neural network based on deep reinforcement learning. Secondly, we delve into an optimization mechanism based on the optimal collaborative chain, as well as a multi-robot bundle adjustment algorithm derived from this mechanism. Lastly, we design and implement a cost function for the decision-making model based on collaborative attention, as well as a reward function for the collaborative model. We further present a learning methodology that combines the weight update processes of both neural networks.Simulation experiments validate the significant enhancements achieved by our algorithm in terms of localization accuracy and mapping performance in multi-robot collaborative backend optimization. Effectively addressing the limitation of improvement in collaboration performance caused by the inability to perceive subsequent collaboration states in the application of attention models in multi-robot collaborative backend optimization. Backend optimization SLAM Multi-robot system Deep reinforcement learning Attention mechanism 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-7797205","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":535022122,"identity":"735bb3e9-a32a-41c3-85db-18b3accf5a31","order_by":0,"name":"Zhaoyi Pei","email":"","orcid":"","institution":"Guangdong Polytechnic of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhaoyi","middleName":"","lastName":"Pei","suffix":""},{"id":535022124,"identity":"d6ab34eb-fe13-43ab-a685-af27ea7ab081","order_by":1,"name":"Zhenghong 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Optimization","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":"Backend optimization, SLAM,Multi-robot system, Deep reinforcement learning, Attention mechanism","lastPublishedDoi":"10.21203/rs.3.rs-7797205/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7797205/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Backend optimization is an essential component of simultaneous localization and mapping (SLAM).Collaborative backend optimization in multi-robot systems refers to the process of extending single-robot collaboration optimization to coordinate and optimize the backend processes of multiple robots working together, enhancing overall system performance and efficiency.In this paper, a deep reinforcement learning model based on attention mechanisms called MAS-AA specifically tailored for collaborative backend optimization in heterogeneous multi-robot systems is proposed, solved the problem of heterogeneous multi robot system collaborative backend optimization not being able to optimize the selection of map points and pose nodes based on constraints between map points and pose nodes considering robot states and attributes. Firstly, we introduce a collaborative attention neural network designed for multi-robot back-end optimization, along with a collaborative decision-making neural network based on deep reinforcement learning. Secondly, we delve into an optimization mechanism based on the optimal collaborative chain, as well as a multi-robot bundle adjustment algorithm derived from this mechanism. Lastly, we design and implement a cost function for the decision-making model based on collaborative attention, as well as a reward function for the collaborative model. We further present a learning methodology that combines the weight update processes of both neural networks.Simulation experiments validate the significant enhancements achieved by our algorithm in terms of localization accuracy and mapping performance in multi-robot collaborative backend optimization. Effectively addressing the limitation of improvement in collaboration performance caused by the inability to perceive subsequent collaboration states in the application of attention models in multi-robot collaborative backend optimization.","manuscriptTitle":"Actor-critic based on Attention Model for Multi-robotCollaborative Backend Optimization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 04:50:19","doi":"10.21203/rs.3.rs-7797205/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"a04277cd-6f13-47ee-a466-0f0dc3d68099","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-01T17:09:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-30 04:50:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7797205","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7797205","identity":"rs-7797205","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}