Edge Computing Driven Scene-aware Intelligent Augmented Reality for Manual Assembly

Edge Computing Driven Scene-aware Intelligent Augmented Reality for Manual Assembly | 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 Edge Computing Driven Scene-aware Intelligent Augmented Reality for Manual Assembly Mingyu Fu, Wei Fang, Shan Gao, Jianhao Hong, Yizhou Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-736006/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Wearable augmented reality (AR) can superimpose virtual models or annotation on real scenes, and which can be utilized in assembly tasks and resulted in high-efficiency and error-avoided manual operations. Nevertheless, most of existing AR-aided assembly operations are based on the predefined visual instruction step-by-step, lacking scene-aware generation for the assembly assistance. To facilitate a friendly AR-aided assembly process, this paper proposed an Edge Computing driven Scene-aware Intelligent AR Assembly (EC-SIARA) system, and smart and worker-centered assistance is available to provide intuitive visual guidance with less cognitive load. In beginning, the connection between the wearable AR glasses and edge computing system is established, which can alleviate the computation burden for the resource-constraint wearable AR glasses, resulting in a high-efficiency deep learning module for scene awareness during the manual assembly process. And then, based on context understanding of the current assembly status, the corresponding augmented instructions can be triggered accordingly, avoiding the operator’s cognitive load to strictly follow the predefined procedure. Finally, quantitative and qualitative experiments are carried out to evaluate the EC-SIARA system, and experimental results show that the proposed method can realize a worker-center AR assembly process, which can improve the assembly efficiency and reduce the occurrence of assembly errors effectively. Mechanical Engineering Intelligent augmented reality scene-aware edge computing manual assembly smart factory Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 09 Aug, 2021 Reviewers invited by journal 22 Jul, 2021 Editor assigned by journal 22 Jul, 2021 First submitted to journal 20 Jul, 2021 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-736006","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":41194568,"identity":"a53e2473-ff39-4c3e-8786-87a8e7725238","order_by":0,"name":"Mingyu Fu","email":"","orcid":"","institution":"Beijing University of Posts and Telecommunications","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mingyu","middleName":"","lastName":"Fu","suffix":""},{"id":41194569,"identity":"0be9e134-471c-4391-9e9d-708d285b10b4","order_by":1,"name":"Wei 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