System-Level Error Propagation and Tail-Risk Amplification in Reference-Based Robotic Navigation

System-Level Error Propagation and Tail-Risk Amplification in Reference-Based Robotic Navigation | 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 System-Level Error Propagation and Tail-Risk Amplification in Reference-Based Robotic Navigation Ning Hu, Maochen Li, Senhao Cao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8811528/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 Image guided robotic navigation systems frequently rely on reference-based geometric perception pipelines, where accurate spatial mapping is established through multi-stage estimation processes. In biplanar X-ray–guided navigation, such pipelines are widely adopted due to their real-time capability and geometric interpretability. However, navigation reliability is often constrained by an overlooked system-level failure mechanism in which installation-induced structural perturbations introduced at the perception stage can be progressively amplified along the perception–reconstruction–execution chain and ultimately dominate execution-level error and tail-risk behavior. This paper investigates this mechanism from a system-level perspective and presents a unified error propagation modeling framework that explicitly characterizes how installation-induced structural perturbations propagate and couple with pixel-level observation noise through biplanar imaging, projection matrix estimation, triangulation, and coordinate mapping. By combining first order analytic uncertainty propagation with large-scale Monte Carlo simulations, we analyze dominant sensitivity channels and quantify worst-case error behavior beyond mean accuracy metrics. The results demonstrate that rotational installation error acts as a primary driver of system level error amplification, whereas translational misalignment of comparable magnitude plays a secondary role under typical biplanar geometries. Moreover, installation-induced structural perturbations fundamentally alter the system’s sensitivity to perception noise, leading to pronounced tail-risk amplification that cannot be captured by additive error models. Real biplanar X-ray bench-top experiments further confirm that the predicted error amplification trends persist under realistic imaging and feature extraction uncertainty. Beyond the specific context of biplanar X-ray navigation, the findings reveal a broader structural limitation of reference-based, multi-stage geometric perception pipelines in robotics. By explicitly modeling system-level error propagation and tail-risk behavior, this work formulates a systematic framework for reliability assessment, sensitivity analysis, and risk-aware design in safety-critical robotic navigation systems. Robotics System-level error propagation Tail-risk amplification Reference-based navigation Geometric perception pipelines Multi-stage geometric estimation Uncertainty propagation Sensitivity analysis Biplanar X-ray navigation Safety-critical robotic systems Full Text Additional Declarations The authors declare no competing interests. Supplementary Files Supplementarypreprint.docx Video.mp4 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. 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-8811528","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587256052,"identity":"c0a25890-03a1-48f4-97e2-b43090ef7875","order_by":0,"name":"Ning 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In biplanar X-ray\u0026ndash;guided navigation, such pipelines are widely adopted due to their real-time capability and geometric interpretability. However, navigation reliability is often constrained by an overlooked system-level failure mechanism in which installation-induced structural perturbations introduced at the perception stage can be progressively amplified along the perception\u0026ndash;reconstruction\u0026ndash;execution chain and ultimately dominate execution-level error and tail-risk behavior. This paper investigates this mechanism from a system-level perspective and presents a unified error propagation modeling framework that explicitly characterizes how installation-induced structural perturbations propagate and couple with pixel-level observation noise through biplanar imaging, projection matrix estimation, triangulation, and coordinate mapping. By combining first order analytic uncertainty propagation with large-scale Monte Carlo simulations, we analyze dominant sensitivity channels and quantify worst-case error behavior beyond mean accuracy metrics. The results demonstrate that rotational installation error acts as a primary driver of system level error amplification, whereas translational misalignment of comparable magnitude plays a secondary role under typical biplanar geometries. Moreover, installation-induced structural perturbations fundamentally alter the system\u0026rsquo;s sensitivity to perception noise, leading to pronounced tail-risk amplification that cannot be captured by additive error models. Real biplanar X-ray bench-top experiments further confirm that the predicted error amplification trends persist under realistic imaging and feature extraction uncertainty. Beyond the specific context of biplanar X-ray navigation, the findings reveal a broader structural limitation of reference-based, multi-stage geometric perception pipelines in robotics. By explicitly modeling system-level error propagation and tail-risk behavior, this work formulates a systematic framework for reliability assessment, sensitivity analysis, and risk-aware design in safety-critical robotic navigation systems.\u003c/p\u003e","manuscriptTitle":"System-Level Error Propagation and Tail-Risk Amplification in Reference-Based Robotic Navigation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 15:19:04","doi":"10.21203/rs.3.rs-8811528/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":"9f908c03-e815-4bf1-9315-18167c2cdb67","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62486002,"name":"Robotics"}],"tags":[],"updatedAt":"2026-02-10T15:19:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 15:19:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8811528","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8811528","identity":"rs-8811528","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}