Simulation-Based Performance Assessment of ORB, YOLOv8, and Picking Strategies for Single-Arm Robot Conveyor Belt Pick-and-Place Operations

Simulation-Based Performance Assessment of ORB, YOLOv8, and Picking Strategies for Single-Arm Robot Conveyor Belt Pick-and-Place Operations | 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 Simulation-Based Performance Assessment of ORB, YOLOv8, and Picking Strategies for Single-Arm Robot Conveyor Belt Pick-and-Place Operations Duc-Kien Huynh, Hong-Chuong Tran This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6252159/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Oct, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract Pick-and-place robots play a crucial role in industrial automation, helping to lower labor costs, minimize errors, and improve production efficiency. Many image processing methods have been proposed to facilitate the pick-and-place operation. However, the performance of these methods is sensitive to the lighting conditions, presence of occlusions, and variations in the object appearance. Although many of these challenges can be overcome through the use of deep learning methods, a direct performance comparison of image processing methods and deep learning methods, coupled with an analysis of different picking strategies, is lacking. The present study addresses this gap by conducting a simulation-based evaluation of the accuracy and processing time of the ORB image processing algorithm and YOLOv8 deep learning model for object recognition. The effects of two different picking strategies (FIFO and Euclidean Distance) on the system throughput are also explored. The simulation results show that YOLOv8 achieves a higher accuracy (98%) and significantly faster processing time (138 ms) than ORB (97.33% accuracy and 715.24 ms processing time). Additionally, the FIFO picking strategy improves the productivity by 13% compared with the Euclidean Distance strategy. Overall, the findings provide valuable insights into optimizing robotic pick-and-place operations in industrial automation settings. Pick-and-place robots YOLOv8 ORB algorithm Picking strategy FIFO Euclidean distance Full Text Supplementary Files VideoS1FIFOvsEUCLIDEANSimulations.mp4 VideoS2SimulationvsExperiment.mp4 Cite Share Download PDF Status: Published Journal Publication published 25 Oct, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Minor Revisions Needed 19 Sep, 2025 Reviewers agreed at journal 25 Jul, 2025 Reviewers invited by journal 25 Mar, 2025 Editor assigned by journal 23 Mar, 2025 First submitted to journal 20 Mar, 2025 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-6252159","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":433937373,"identity":"4b76ff24-84df-47ed-b2ea-20c68f897b34","order_by":0,"name":"Duc-Kien Huynh","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Duc-Kien","middleName":"","lastName":"Huynh","suffix":""},{"id":433937374,"identity":"78f368fb-4c93-4491-ae09-fb7aadd43c42","order_by":1,"name":"Hong-Chuong Tran","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYBACAwhlA+WyEaWFGUSlMfCAGUAtPERqOUyCFnP288c+/NxxPs9eIv8Aw4eywwz2Egn4tVj2JDPP7D1zu5hHIpmBccY5oHWEtBjcYGZm4G27ndgD1MLM2wbUIk2EFsa/becgWv4SqwVo+AGIFkaitJxJNmaWbUtO7Dnz2OBgz7l0Hp77DwhoOX7wMePbNrvE9vbEhw9+lFnLsfccwK8FBYDUEoyWUTAKRsEoGAVEAAAJMjx7fUR7lwAAAABJRU5ErkJggg==","orcid":"","institution":"National Taipei University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Hong-Chuong","middleName":"","lastName":"Tran","suffix":""}],"badges":[],"createdAt":"2025-03-18 10:30:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6252159/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6252159/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00170-025-16781-4","type":"published","date":"2025-10-25T16:16:59+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":94490723,"identity":"74384df7-2153-41e7-8a78-0a6400fece5f","added_by":"auto","created_at":"2025-10-27 17:14:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2241559,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptrobotV2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6252159/v1_covered_b079593d-1927-4ba4-bad8-bffbd278dabc.pdf"},{"id":80022029,"identity":"576f8aa3-e0ec-4f2b-a906-2aa39f0c0594","added_by":"auto","created_at":"2025-04-07 05:08:38","extension":"mp4","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":2203609,"visible":true,"origin":"","legend":"","description":"","filename":"VideoS1FIFOvsEUCLIDEANSimulations.mp4","url":"https://assets-eu.researchsquare.com/files/rs-6252159/v1/5b6b9b6ee1a46dd48d81043b.mp4"},{"id":80021230,"identity":"df45863a-175d-42f4-b8f7-64dfde4105f9","added_by":"auto","created_at":"2025-04-07 05:00:38","extension":"mp4","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":4520633,"visible":true,"origin":"","legend":"","description":"","filename":"VideoS2SimulationvsExperiment.mp4","url":"https://assets-eu.researchsquare.com/files/rs-6252159/v1/94e564f444d170eb58add76a.mp4"}],"financialInterests":"","formattedTitle":"Simulation-Based Performance Assessment of ORB, YOLOv8, and Picking Strategies for Single-Arm Robot Conveyor Belt Pick-and-Place Operations","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Pick-and-place robots, YOLOv8, ORB algorithm, Picking strategy, FIFO, Euclidean distance","lastPublishedDoi":"10.21203/rs.3.rs-6252159/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6252159/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePick-and-place robots play a crucial role in industrial automation, helping to lower labor costs, minimize errors, and improve production efficiency. 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