Data-driven trajectory performance prediction for industrial robots via multi-modal retrieval

Data-driven trajectory performance prediction for industrial robots via multi-modal retrieval | 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 Data-driven trajectory performance prediction for industrial robots via multi-modal retrieval Gustavo Barros, Noel Appel, Felix Thomas, Bernd Kuhlenkötter This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9034000/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 5 You are reading this latest preprint version Abstract Evaluating industrial robot performance typically involves costly individual tests or standardized procedures that cover only a limited set of motion sequences. This makes it difficult to transfer measured accuracy to new trajectories, especially when robot configurations or workspace regions change. To enable data-driven performance prediction at scale, this work introduces a publicly available database of over 30,000 measured trajectories of an ABB IRB4400 industrial robot. Based on this database, a multi-modal retrieval framework is proposed that identifies similar motion sequences and predicts trajectory performance from their measured deviations. The framework employs a two-stage pipeline: embedding-based candidate retrieval across multiple motion modalities followed by constrained Dynamic Time Warping refinement for precise spatio-temporal alignment. Experimental evaluation on 500 randomly sampled trajectories demonstrates that multi-modal retrieval outperforms a scalar-feature nearest-neighbor baseline, and that segment-level decomposition improves both prediction accuracy and computational efficiency. The framework operates without parametric model fitting and can incorporate additional measurements without structural modification, allowing predictive capability to grow with increasing database coverage. Industrial robotics Performance prediction Trajectory similarity Dynamic Time Warping Retrieval-based prediction Robot motion database Full Text Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Major Revisions Needed 11 May, 2026 Reviewers agreed at journal 15 Mar, 2026 Reviewers invited by journal 13 Mar, 2026 Editor assigned by journal 05 Mar, 2026 First submitted to journal 04 Mar, 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-9034000","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":605843339,"identity":"c515c5ed-ccbf-4d58-ad84-76e449a65136","order_by":0,"name":"Gustavo Barros","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYBACAwhlIwOmGBtAiDgtaTwkazlMghZz9rPPPnz4c57HnP8A22feHQyy/YS0WPakG8+c2Xabx3JGAvNs3jMMxjMJWWNwII2ZmbfhNo/BDQYgo40hccMBQlrOP2Nm5vlzjsfg/AGIlv0EtdwA2sLDdoDH4EAC1BaCfpnxjJlxZlsy0C+JzYxzz0gYzyBkizl/GjPDhz92cub8hw8zvN1hI9vfQMgauAshMSJBrHoGeJyOglEwCkbBKMAEAK0iOqHzNoxPAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-4304-8288","institution":"Ruhr University Bochum: Ruhr-Universitat Bochum","correspondingAuthor":true,"prefix":"","firstName":"Gustavo","middleName":"","lastName":"Barros","suffix":""},{"id":605843340,"identity":"be839f51-6eed-448c-82a9-54f447096064","order_by":1,"name":"Noel Appel","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Noel","middleName":"","lastName":"Appel","suffix":""},{"id":605843341,"identity":"f728fad7-84ac-4c8f-bf9d-efeabaa165aa","order_by":2,"name":"Felix Thomas","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Felix","middleName":"","lastName":"Thomas","suffix":""},{"id":605843342,"identity":"f10653c2-2d15-46a9-9372-27e2f3645654","order_by":3,"name":"Bernd Kuhlenkötter","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Bernd","middleName":"","lastName":"Kuhlenkötter","suffix":""}],"badges":[],"createdAt":"2026-03-04 22:02:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9034000/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9034000/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108182834,"identity":"3e37983f-cd9e-444a-b92f-8ced21e905fd","added_by":"auto","created_at":"2026-04-30 08:59:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1620484,"visible":true,"origin":"","legend":"","description":"","filename":"JAMTD2601392.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9034000/v1_covered_64982fa0-2352-4bd4-9ba0-8eda979ff46b.pdf"}],"financialInterests":"","formattedTitle":"Data-driven trajectory performance prediction for industrial robots via multi-modal retrieval","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":true,"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":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Industrial robotics, Performance prediction, Trajectory similarity, Dynamic Time Warping, Retrieval-based prediction, Robot motion database","lastPublishedDoi":"10.21203/rs.3.rs-9034000/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9034000/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Evaluating industrial robot performance typically involves costly individual tests or standardized procedures that cover only a limited set of motion sequences. 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