{"paper_id":"960dbe32-b9d7-40da-8745-b3bcfce0d3b2","body_text":"Real Time Predictive Maintenance of Collaborative Robotic Arms Using a Physics Informed Digital Twin and Agentic Edge Computing | 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 Real Time Predictive Maintenance of Collaborative Robotic Arms Using a Physics Informed Digital Twin and Agentic Edge Computing Zacheous Aasa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8941768/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract The integration of Industry 5.0 principles necessitates a transition from reactive to proactive maintenance models in automated manufacturing. This research addresses the critical challenge of synchronization errors caused by latency in cyber-physical systems by introducing a high-fidelity, Physics-Informed Digital Twin (P-DT) approach for real-time predictive maintenance of industrial robotic arms. The proposed architecture utilizes an Agentic Edge Computing layer to offload the computationally intensive task processing from the cloud, ensuring sub-millisecond processing of multi-modal sensor data such as torque, vibration, and thermal patterns. A new Deep Reinforcement Learning (DRL) method is also developed at the edge to optimize resource allocation and predict Remaining Useful Life (RUL) under stochastic workloads. By embedding physical kinematic constraints within the neural network’s loss function, this approach shows a 28% relative improvement in fault detection sensitivity over traditional data-driven models. Simulation results in dense industrial settings show that the P-DT approach preserves a 99.7% level of synchronization accuracy while lowering unplanned downtime by 34%. Additionally, the approach facilitates proactive self-correction capabilities to compensate for mechanical degradation by adjusting motion plans. These results offer a sound solution for the deployment of autonomous and self-healing robotic systems in latency-sensitive industrial settings. Digital Twin Edge Computing Predictive Maintenance Industrial Robotic Arms Physics Informed Neural Networks Deep Reinforcement Learning Industry 5.0 Cyber Physical Systems Real Time Resource Orchestration Anomaly Detection Remaining Useful Life Smart Manufacturing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 30 Mar, 2026 Reviews received at journal 29 Mar, 2026 Reviewers agreed at journal 27 Mar, 2026 Reviews received at journal 22 Mar, 2026 Reviews received at journal 21 Mar, 2026 Reviewers agreed at journal 21 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers invited by journal 16 Mar, 2026 Editor invited by journal 05 Mar, 2026 Editor assigned by journal 05 Mar, 2026 Submission checks completed at journal 04 Mar, 2026 First submitted to journal 26 Feb, 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. 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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-8941768\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":601152551,\"identity\":\"7825c058-a865-4822-8997-42608c79c756\",\"order_by\":0,\"name\":\"Zacheous Aasa\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYHACNiR2BRAzMzeQouUMSAsjKVoY28Akfi3y7YefPfhQcdiegf3w4w8/59VG87cDtfyo2IZTC2NPmrnhjDOHExt40swke7cdz51xmLGBsefMbZxamBly2KR52w4nMDAkmDHwbjuW2wDUwszYhlsLG/8bNum/bUCH8T///PHvnGO58wlp4ZEA2sLYBlQmkWMgzdtQk7uBkBYJiWdmkj1n0hPbJN6UScscO5C7EajlID6/yPcnP5P4UWFtz8+fvvnjm5q63HnnDx988KMCtxaEpyDUYTB5gLB6BKgjRfEoGAWjYBSMEAAAvS9W0AOCaloAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Ladoke Akintola University of Technology, LAUTECH\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Zacheous\",\"middleName\":\"\",\"lastName\":\"Aasa\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-02-22 23:08:04\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-8941768/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-8941768/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":104402649,\"identity\":\"cf26d220-6d31-42c4-8263-a851a9d306ec\",\"added_by\":\"auto\",\"created_at\":\"2026-03-11 12:15:59\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":613376,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"AMENDEDZacheousRealTimePredictiveMaintenanceofCollaborativeRoboticArms06012345.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8941768/v1_covered_3722e5e0-8015-408f-aa11-dd19cdd229d1.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Real Time Predictive Maintenance of Collaborative Robotic Arms Using a Physics Informed Digital Twin and Agentic Edge Computing\",\"fulltext\":[],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"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\":\"info@researchsquare.com\",\"identity\":\"discover-electronics\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"Learn more about [Discover Electronics](https://www.springer.com/journal/44291)\",\"snPcode\":\"44291\",\"submissionUrl\":\"https://submission.nature.com/new-submission/44291\",\"title\":\"Discover Electronics\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Discover Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Digital Twin, Edge Computing, Predictive Maintenance, Industrial Robotic Arms, Physics Informed Neural Networks, Deep Reinforcement Learning, Industry 5.0, Cyber Physical Systems, Real Time Resource Orchestration, Anomaly Detection, Remaining Useful Life, Smart Manufacturing\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8941768/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8941768/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"The integration of Industry 5.0 principles necessitates a transition from reactive to proactive maintenance models in automated manufacturing. 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