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<!DOCTYPE html> <html lang="en" class="no-js"> <head> <meta charset="UTF-8"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <meta name="applicable-device" content="pc,mobile"> <meta name="viewport" content="width=device-width, initial-scale=1"> <meta name="robots" content="max-image-preview:large"> <meta name="access" content="Yes"> <meta name="360-site-verification" content="1268d79b5e96aecf3ff2a7dac04ad990" /> <title>A planning and execution framework for robotic assembly with deformable objects using a centralized database based on the Rαβγ categorization | Journal of Intelligent Manufacturing </title> <meta name="twitter:site" content="@SpringerLink"/> <meta name="twitter:card" content="summary_large_image"/> <meta name="twitter:image:alt" content="Content cover image"/> <meta name="twitter:title" content="A planning and execution framework for robotic assembly with deformable objects using a centralized database based on the Rαβγ categorization"/> <meta name="twitter:description" content="Journal of Intelligent Manufacturing - Automation of process planning and execution of robotic assembly can lead to process optimization and shorter setup times. Several such automation frameworks..."/> <meta name="twitter:image" content="https://static-content.springer.com/image/art%3A10.1007%2Fs10845-025-02578-5/MediaObjects/10845_2025_2578_Figa_HTML.png"/> <meta name="journal_id" content="10845"/> <meta name="dc.title" content="A planning and execution framework for robotic assembly with deformable objects using a centralized database based on the Rαβγ categorization"/> <meta name="dc.source" content="Journal of Intelligent Manufacturing 2025"/> <meta name="dc.format" content="text/html"/> <meta name="dc.publisher" content="Springer"/> <meta name="dc.date" content="2025-03-06"/> <meta name="dc.type" content="OriginalPaper"/> <meta name="dc.language" content="En"/> <meta name="dc.copyright" content="2025 The Author(s)"/> <meta name="dc.rights" content="2025 The Author(s)"/> <meta name="dc.rightsAgent" content="journalpermissions@springernature.com"/> <meta name="dc.description" content="Automation of process planning and execution of robotic assembly can lead to process optimization and shorter setup times. Several such automation frameworks have been developed for the assembly of rigid objects. However, many products require assembly with deformable objects. Robotic assembly with deformable objects typically incurs more complex dynamics and requires more collaboration during execution than rigid object assembly. In addition, process documentation includes more documents that are less structured. The current research suggests a data-driven planning and execution automation framework suitable for robotic assembly with deformable objects. The framework includes the three required modules: data extraction, process planning, and process execution. The modules interact with a central database constructed according to the Rαβγ ontology. Data extraction is based on commonly used manufacturing documents. Process planning is based on parametrized hybrid automata models, which encompass process and collaboration complexity using two layers: assembly operations and robotic skills. Process execution integrates a digital twin for sequence validation, process adaptation, and monitoring. The framework was successfully demonstrated in a small factory environment with three case studies for products with deformable objects: two smart light boards which include parts with plastic deformations (electric wires) and a medical infusion kit with parts with elastic deformations (tube, connectors). The framework facilitated optimized planning with significant reuse of assembly operations for all products. Both light boards had a high rate of assembly operation reuse (78%, 86%). The medical infusion kit had a somewhat lower rate (62%) due to the need for dedicated operations."/> <meta name="prism.issn" content="1572-8145"/> <meta name="prism.publicationName" content="Journal of Intelligent Manufacturing"/> <meta name="prism.publicationDate" content="2025-03-06"/> <meta name="prism.section" content="OriginalPaper"/> <meta name="prism.startingPage" content="1"/> <meta name="prism.endingPage" content="21"/> <meta name="prism.copyright" content="2025 The Author(s)"/> <meta name="prism.rightsAgent" content="journalpermissions@springernature.com"/> <meta name="prism.url" content="https://link.springer.com/article/10.1007/s10845-025-02578-5"/> <meta name="prism.doi" content="doi:10.1007/s10845-025-02578-5"/> <meta name="citation_pdf_url" content="https://link.springer.com/content/pdf/10.1007/s10845-025-02578-5.pdf"/> <meta name="citation_fulltext_html_url" content="https://link.springer.com/article/10.1007/s10845-025-02578-5"/> <meta name="citation_journal_title" content="Journal of Intelligent Manufacturing"/> <meta name="citation_journal_abbrev" content="J Intell Manuf"/> <meta name="citation_publisher" content="Springer US"/> <meta name="citation_issn" content="1572-8145"/> <meta name="citation_title" content="A planning and execution framework for robotic assembly with deformable objects using a centralized database based on the Rαβγ categorization"/> <meta name="citation_online_date" content="2025/03/06"/> <meta name="citation_firstpage" content="1"/> <meta name="citation_lastpage" content="21"/> <meta name="citation_article_type" content="Article"/> <meta name="citation_fulltext_world_readable" content=""/> <meta name="citation_language" content="en"/> <meta name="dc.identifier" content="doi:10.1007/s10845-025-02578-5"/> <meta name="DOI" content="10.1007/s10845-025-02578-5"/> <meta name="size" content="231451"/> <meta name="citation_doi" content="10.1007/s10845-025-02578-5"/> <meta name="citation_springer_api_url" content="http://api.springer.com/xmldata/jats?q=doi:10.1007/s10845-025-02578-5&api_key="/> <meta name="description" content="Automation of process planning and execution of robotic assembly can lead to process optimization and shorter setup times. Several such automation framewor"/> <meta name="dc.creator" content="Shneor, Ran"/> <meta name="dc.creator" content="Naveh, Gali"/> <meta name="dc.creator" content="Ben-David, Shir"/> <meta name="dc.creator" content="Shvarzman, Bar"/> <meta name="dc.creator" content="Mann, Zachi"/> <meta name="dc.creator" content="Greenberg, Alex"/> <meta name="dc.creator" content="Efrat, Yotam"/> <meta name="dc.creator" content="Einav, Omer"/> <meta name="dc.creator" content="Berman, Sigal"/> <meta name="dc.subject" content="Production"/> <meta name="dc.subject" content="Manufacturing, Machines, Tools, Processes"/> <meta name="dc.subject" content="Control, Robotics, Mechatronics"/> <meta name="citation_reference" content="citation_journal_title=Archives of Computational Methods in Engineering; citation_title=Optimization of assembly sequence planning using soft computing approaches: A review; citation_author=MA Abdullah, A Rashid, Z Ghazalli; citation_volume=26; citation_issue=2; citation_publication_date=2018; citation_pages=461-474; citation_doi=10.1007/s11831-018-9250-y; citation_id=CR1"/> <meta name="citation_reference" content="Acker, J., & Henrich, D. 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Several such automation frameworks have been developed for the assembly of rigid objects. However, many products require assembly with deformable objects. Robotic assembly with deformable objects typically incurs more complex dynamics and requires more collaboration during execution than rigid object assembly. In addition, process documentation includes more documents that are less structured. The current research suggests a data-driven planning and execution automation framework suitable for robotic assembly with deformable objects. The framework includes the three required modules: data extraction, process planning, and process execution. The modules interact with a central database constructed according to the Rαβγ ontology. Data extraction is based on commonly used manufacturing documents. Process planning is based on parametrized hybrid automata models, which encompass process and collaboration complexity using two layers: assembly operations and robotic skills. Process execution integrates a digital twin for sequence validation, process adaptation, and monitoring. The framework was successfully demonstrated in a small factory environment with three case studies for products with deformable objects: two smart light boards which include parts with plastic deformations (electric wires) and a medical infusion kit with parts with elastic deformations (tube, connectors). The framework facilitated optimized planning with significant reuse of assembly operations for all products. Both light boards had a high rate of assembly operation reuse (78%, 86%). The medical infusion kit had a somewhat lower rate (62%) due to the need for dedicated operations. 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Several such automation frameworks have been developed for the assembly of rigid objects. However, many products require assembly with deformable objects. Robotic assembly with deformable objects typically incurs more complex dynamics and requires more collaboration during execution than rigid object assembly. In addition, process documentation includes more documents that are less structured. The current research suggests a data-driven planning and execution automation framework suitable for robotic assembly with deformable objects. The framework includes the three required modules: data extraction, process planning, and process execution. The modules interact with a central database constructed according to the Rαβγ ontology. Data extraction is based on commonly used manufacturing documents. Process planning is based on parametrized hybrid automata models, which encompass process and collaboration complexity using two layers: assembly operations and robotic skills. Process execution integrates a digital twin for sequence validation, process adaptation, and monitoring. The framework was successfully demonstrated in a small factory environment with three case studies for products with deformable objects: two smart light boards which include parts with plastic deformations (electric wires) and a medical infusion kit with parts with elastic deformations (tube, connectors). The framework facilitated optimized planning with significant reuse of assembly operations for all products. Both light boards had a high rate of assembly operation reuse (78%, 86%). 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href="https://www.editorialmanager.com/jims" class="app-article-masthead__submission-link" data-track="click_submit_manuscript" data-track-context="article masthead on springerlink article page" data-track-action="submit manuscript" data-track-label="link"> Submit manuscript <svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-arrow-right-medium"></use></svg> </a> </div> </div> </div> </section> <div class="c-article-main u-container u-mt-24 u-mb-32 l-with-sidebar" id="main-content" data-component="article-container"> <main class="u-serif js-main-column" data-track-component="article body"> <div class="c-context-bar u-hide" data-test="context-bar" data-context-bar aria-hidden="true"> <div class="c-context-bar__container u-container"> <div class="c-context-bar__title"> A planning and execution framework for robotic assembly with deformable objects using a centralized database based on the Rαβγ categorization </div> <div data-test="inCoD" data-track-context="sticky banner"> <div class="c-pdf-container"> <div class="c-pdf-download u-clear-both u-mb-16"> <a href="/content/pdf/10.1007/s10845-025-02578-5.pdf" class="u-button u-button--full-width u-button--primary u-justify-content-space-between c-pdf-download__link" data-article-pdf="true" data-readcube-pdf-url="true" data-test="pdf-link" data-draft-ignore="true" data-track="content_download" data-track-type="article pdf download" data-track-action="download pdf" data-track-label="button" data-track-external download> <span class="c-pdf-download__text">Download PDF</span> <svg aria-hidden="true" focusable="false" width="16" height="16" class="u-icon"><use xlink:href="#icon-eds-i-download-medium"/></svg> </a> </div> </div> </div> </div> </div> <div class="c-article-header"> <header> <ul class="c-article-author-list c-article-author-list--short" data-test="authors-list" data-component-authors-activator="authors-list"><li class="c-article-author-list__item"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Ran-Shneor-Aff1" data-author-popup="auth-Ran-Shneor-Aff1" data-author-search="Shneor, Ran" data-corresp-id="c1">Ran Shneor<svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-mail-medium"></use></svg></a><span class="u-js-hide"> <a class="js-orcid" href="http://orcid.org/0000-0001-7736-4193"><span class="u-visually-hidden">ORCID: </span>orcid.org/0000-0001-7736-4193</a></span><sup class="u-js-hide"><a href="#Aff1">1</a></sup>, </li><li class="c-article-author-list__item"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Gali-Naveh-Aff2" data-author-popup="auth-Gali-Naveh-Aff2" data-author-search="Naveh, Gali">Gali Naveh</a><span class="u-js-hide"> <a class="js-orcid" href="http://orcid.org/0000-0002-7002-2606"><span class="u-visually-hidden">ORCID: </span>orcid.org/0000-0002-7002-2606</a></span><sup class="u-js-hide"><a href="#Aff2">2</a></sup>, </li><li class="c-article-author-list__item c-article-author-list__item--hide-small-screen"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Shir-Ben_David-Aff1" data-author-popup="auth-Shir-Ben_David-Aff1" data-author-search="Ben-David, Shir">Shir Ben-David</a><sup class="u-js-hide"><a href="#Aff1">1</a></sup>, </li><li class="c-article-author-list__item c-article-author-list__item--hide-small-screen"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Bar-Shvarzman-Aff1" data-author-popup="auth-Bar-Shvarzman-Aff1" data-author-search="Shvarzman, Bar">Bar Shvarzman</a><sup class="u-js-hide"><a href="#Aff1">1</a></sup>, </li><li class="c-article-author-list__item c-article-author-list__item--hide-small-screen"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Zachi-Mann-Aff3" data-author-popup="auth-Zachi-Mann-Aff3" data-author-search="Mann, Zachi">Zachi Mann</a><sup class="u-js-hide"><a href="#Aff3">3</a></sup>, </li><li class="c-article-author-list__item c-article-author-list__item--hide-small-screen"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Alex-Greenberg-Aff3" data-author-popup="auth-Alex-Greenberg-Aff3" data-author-search="Greenberg, Alex">Alex Greenberg</a><sup class="u-js-hide"><a href="#Aff3">3</a></sup>, </li><li class="c-article-author-list__item c-article-author-list__item--hide-small-screen"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Yotam-Efrat-Aff4" data-author-popup="auth-Yotam-Efrat-Aff4" data-author-search="Efrat, Yotam">Yotam Efrat</a><sup class="u-js-hide"><a href="#Aff4">4</a></sup>, </li><li class="c-article-author-list__item c-article-author-list__item--hide-small-screen"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Omer-Einav-Aff4" data-author-popup="auth-Omer-Einav-Aff4" data-author-search="Einav, Omer">Omer Einav</a><sup class="u-js-hide"><a href="#Aff4">4</a></sup> & </li><li class="c-article-author-list__show-more" aria-label="Show all 9 authors for this article" title="Show all 9 authors for this article">…</li><li class="c-article-author-list__item"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Sigal-Berman-Aff1" data-author-popup="auth-Sigal-Berman-Aff1" data-author-search="Berman, Sigal">Sigal Berman</a><span class="u-js-hide"> <a class="js-orcid" href="http://orcid.org/0000-0001-7717-7259"><span class="u-visually-hidden">ORCID: </span>orcid.org/0000-0001-7717-7259</a></span><sup class="u-js-hide"><a href="#Aff1">1</a></sup> </li></ul><button aria-expanded="false" class="c-article-author-list__button"><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-down-medium"></use></svg><span>Show authors</span></button> <div data-test="article-metrics"> <ul class="app-article-metrics-bar u-list-reset"> <li class="app-article-metrics-bar__item"> <p class="app-article-metrics-bar__count"><svg class="u-icon app-article-metrics-bar__icon" width="24" height="24" aria-hidden="true" focusable="false"> <use xlink:href="#icon-eds-i-accesses-medium"></use> </svg>399 <span class="app-article-metrics-bar__label">Accesses</span></p> </li> <li class="app-article-metrics-bar__item app-article-metrics-bar__item--metrics"> <p class="app-article-metrics-bar__details"><a href="/article/10.1007/s10845-025-02578-5/metrics" data-track="click" data-track-action="view metrics" data-track-label="link" rel="nofollow">Explore all metrics <svg class="u-icon app-article-metrics-bar__arrow-icon" width="24" height="24" aria-hidden="true" focusable="false"> <use xlink:href="#icon-eds-i-arrow-right-medium"></use> </svg></a></p> </li> </ul> </div> <div class="u-mt-32"> </div> </header> </div> <div data-article-body="true" data-track-component="article body" class="c-article-body"> <section lang="en"><div class="c-article-section" id="Abs1-section"><div class="c-article-section__content" id="Abs1-content"><h3 class="c-article__sub-heading" data-test="abstract-sub-heading">Abstract</h3><p>Automation of process planning and execution of robotic assembly can lead to process optimization and shorter setup times. Several such automation frameworks have been developed for the assembly of rigid objects. However, many products require assembly with deformable objects. Robotic assembly with deformable objects typically incurs more complex dynamics and requires more collaboration during execution than rigid object assembly. In addition, process documentation includes more documents that are less structured. The current research suggests a data-driven planning and execution automation framework suitable for robotic assembly with deformable objects. The framework includes the three required modules: data extraction, process planning, and process execution. The modules interact with a central database constructed according to the Rαβγ ontology. Data extraction is based on commonly used manufacturing documents. Process planning is based on parametrized hybrid automata models, which encompass process and collaboration complexity using two layers: assembly operations and robotic skills. Process execution integrates a digital twin for sequence validation, process adaptation, and monitoring. The framework was successfully demonstrated in a small factory environment with three case studies for products with deformable objects: two smart light boards which include parts with plastic deformations (electric wires) and a medical infusion kit with parts with elastic deformations (tube, connectors). The framework facilitated optimized planning with significant reuse of assembly operations for all products. Both light boards had a high rate of assembly operation reuse (78%, 86%). The medical infusion kit had a somewhat lower rate (62%) due to the need for dedicated operations.</p><h3 class="c-article__sub-heading" data-test="abstract-sub-heading">Graphical abstract</h3><div class="c-article-section__figure" data-test="figure" data-container-section="figure"><figure><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><picture><source type="image/webp" srcset="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs10845-025-02578-5/MediaObjects/10845_2025_2578_Figa_HTML.png?as=webp"><img src="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs10845-025-02578-5/MediaObjects/10845_2025_2578_Figa_HTML.png" alt="" loading="lazy" width="685" height="271"></picture></div></div></figure></div></div></div></section> <div data-test="cobranding-download"> </div> <section aria-labelledby="inline-recommendations" data-title="Inline Recommendations" class="c-article-recommendations" data-track-component="inline-recommendations"> <h3 class="c-article-recommendations-title" id="inline-recommendations">Similar content being viewed by others</h3> <div class="c-article-recommendations-list"> <div class="c-article-recommendations-list__item"> <article class="c-article-recommendations-card" itemscope itemtype="http://schema.org/ScholarlyArticle"> <div class="c-article-recommendations-card__img"><img src="https://media.springernature.com/w92h120/springer-static/cover-hires/book/978-3-319-58694-6?as=webp" loading="lazy" alt=""></div> <div class="c-article-recommendations-card__main"> <h3 class="c-article-recommendations-card__heading" itemprop="name headline"> <a class="c-article-recommendations-card__link" itemprop="url" href="https://link.springer.com/10.1007/978-3-319-58694-6_36?fromPaywallRec=false" data-track="select_recommendations_1" data-track-context="inline recommendations" data-track-action="click recommendations inline - 1" data-track-label="10.1007/978-3-319-58694-6_36">Semantic Data Integration for Industry 4.0 Standards </a> </h3> <div class="c-article-meta-recommendations" data-test="recommendation-info"> <span class="c-article-meta-recommendations__item-type">Chapter</span> <span class="c-article-meta-recommendations__date">© 2017</span> </div> </div> </article> </div> <div class="c-article-recommendations-list__item"> <article class="c-article-recommendations-card" itemscope itemtype="http://schema.org/ScholarlyArticle"> <div class="c-article-recommendations-card__img"><img src="https://media.springernature.com/w92h120/springer-static/cover-hires/book/978-3-319-51100-9?as=webp" loading="lazy" alt=""></div> <div class="c-article-recommendations-card__main"> <h3 class="c-article-recommendations-card__heading" itemprop="name headline"> <a class="c-article-recommendations-card__link" itemprop="url" href="https://link.springer.com/10.1007/978-3-319-51100-9_33?fromPaywallRec=false" data-track="select_recommendations_2" data-track-context="inline recommendations" data-track-action="click recommendations inline - 2" data-track-label="10.1007/978-3-319-51100-9_33">A Highly Flexible, Distributed Data Analysis Framework for Industry 4.0 Manufacturing Systems </a> </h3> <div class="c-article-meta-recommendations" data-test="recommendation-info"> <span class="c-article-meta-recommendations__item-type">Chapter</span> <span class="c-article-meta-recommendations__date">© 2017</span> </div> </div> </article> </div> <div class="c-article-recommendations-list__item"> <article class="c-article-recommendations-card" itemscope itemtype="http://schema.org/ScholarlyArticle"> <div class="c-article-recommendations-card__img"><img src="https://media.springernature.com/w92h120/springer-static/cover-hires/book/978-981-99-0301-6?as=webp" loading="lazy" alt=""></div> <div class="c-article-recommendations-card__main"> <h3 class="c-article-recommendations-card__heading" itemprop="name headline"> <a class="c-article-recommendations-card__link" itemprop="url" href="https://link.springer.com/10.1007/978-981-99-0301-6_33?fromPaywallRec=false" data-track="select_recommendations_3" data-track-context="inline recommendations" data-track-action="click recommendations inline - 3" data-track-label="10.1007/978-981-99-0301-6_33">Design of an Improved Process Mining Algorithm for Manufacturing Companies with Industrial Robots </a> </h3> <div class="c-article-meta-recommendations" data-test="recommendation-info"> <span class="c-article-meta-recommendations__item-type">Chapter</span> <span class="c-article-meta-recommendations__date">© 2023</span> </div> </div> </article> </div> </div> </section> <script> window.dataLayer = window.dataLayer || []; window.dataLayer.push({ recommendations: { recommender: 'semantic', model: 'specter', policy_id: 'NA', timestamp: 1743006677, embedded_user: 'null' } }); </script> <div class="app-card-service" data-test="article-checklist-banner"> <div> <a class="app-card-service__link" data-track="click_presubmission_checklist" data-track-context="article page top of reading companion" data-track-category="pre-submission-checklist" data-track-action="clicked article page checklist banner test 2 old version" data-track-label="link" href="https://beta.springernature.com/pre-submission?journalId=10845" data-test="article-checklist-banner-link"> <span class="app-card-service__link-text">Use our pre-submission checklist</span> <svg class="app-card-service__link-icon" aria-hidden="true" focusable="false"><use xlink:href="#icon-eds-i-arrow-right-small"></use></svg> </a> <p class="app-card-service__description">Avoid common mistakes on your manuscript.</p> </div> <div class="app-card-service__icon-container"> <svg class="app-card-service__icon" aria-hidden="true" focusable="false"> <use xlink:href="#icon-eds-i-clipboard-check-medium"></use> </svg> </div> </div> <div class="main-content"> <section data-title="Introduction"><div class="c-article-section" id="Sec1-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Sec1">Introduction</h2><div class="c-article-section__content" id="Sec1-content"><p>Modern robots possess the dexterity required for many assembly tasks, including assembly with deformable objects, i.e., assembly where some or all the assembled objects that form the product are deformable (Billard & Kragic, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2019" title="Billard, A., & Kragic, D. (2019). Trends and challenges in robot manipulation. Science. 
 https://doi.org/10.1126/science.aat8414
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR6" id="ref-link-section-d64739081e455">2019</a>). Deformable objects are made from materials that can change shape or size under external forces (e.g., robotic arm manoeuvres and interaction with other objects). With deformable objects, plastic (lasting shape change) and elastic (temporary shape change) deformations or their combination can be induced during object manipulation (Sanchez et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2018" title="Sanchez, J., Corrales, J. A., Bouzgarrou, B. C., & Mezouar, Y. (2018). Robotic manipulation and sensing of deformable objects in domestic and industrial applications: A survey. The International Journal of Robotics Research, 37(7), 688–716. 
 https://doi.org/10.1177/0278364918779698
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR54" id="ref-link-section-d64739081e458">2018</a>). During assembly, these deformations come in parallel to object pose changes, making assembly process dynamics complex.</p><p>Over the years, several literature reviews of research and industrial applications of robotic manipulation of deformable objects were conducted (Herguedas et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2019" title="Herguedas, R., Lopez-Nicolas, G., Aragues, R., & Sagues, C. (2019). Survey on multi-robot manipulation of deformable objects. In 2019 24th IEEE International conference on emerging technologies and factory automation (ETFA) (pp. 977–984). 
 https://doi.org/10.1109/etfa.2019.8868987
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR21" id="ref-link-section-d64739081e464">2019</a>; Jiménez, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2012" title="Jiménez, P. (2012). Survey on model-based manipulation planning of deformable objects. Robotics and Computer-Integrated Manufacturing, 28(2), 154–163. 
 https://doi.org/10.1016/j.rcim.2011.08.002
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR27" id="ref-link-section-d64739081e467">2012</a>; Saadat & Nan, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2002" title="Saadat, M., & Nan, P. (2002). Industrial applications of automatic manipulation of flexible materials. Industrial Robot: An International Journal, 29(5), 434–442. 
 https://doi.org/10.1108/01439910210440255
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR53" id="ref-link-section-d64739081e470">2002</a>; Sanchez et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2018" title="Sanchez, J., Corrales, J. A., Bouzgarrou, B. C., & Mezouar, Y. (2018). Robotic manipulation and sensing of deformable objects in domestic and industrial applications: A survey. The International Journal of Robotics Research, 37(7), 688–716. 
 https://doi.org/10.1177/0278364918779698
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR54" id="ref-link-section-d64739081e473">2018</a>). Jiménez (<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2012" title="Jiménez, P. (2012). Survey on model-based manipulation planning of deformable objects. Robotics and Computer-Integrated Manufacturing, 28(2), 154–163. 
 https://doi.org/10.1016/j.rcim.2011.08.002
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR27" id="ref-link-section-d64739081e476">2012</a>) classified modelling approaches for deformable objects according to the object’s geometric dimensions (i.e., linear, planar, and volumetric) and the type of manipulation (object path planning, folding/unfolding, topological modifications to the object, assembly). Saadat and Nan (<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2002" title="Saadat, M., & Nan, P. (2002). Industrial applications of automatic manipulation of flexible materials. Industrial Robot: An International Journal, 29(5), 434–442. 
 https://doi.org/10.1108/01439910210440255
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR53" id="ref-link-section-d64739081e480">2002</a>) similarly classified industrial applications based on the object’s geometrical dimensions, with sub-classifications for the type of material (e.g., leather, cable/wire), the type of industry (e.g., food, electronics), and the type of object manipulation. Sanchez et al. (<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2018" title="Sanchez, J., Corrales, J. A., Bouzgarrou, B. C., & Mezouar, Y. (2018). Robotic manipulation and sensing of deformable objects in domestic and industrial applications: A survey. The International Journal of Robotics Research, 37(7), 688–716. 
 https://doi.org/10.1177/0278364918779698
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR54" id="ref-link-section-d64739081e483">2018</a>) proposed an adaptation of the classification of the object’s geometry by adding a category of non-planar surfaces appropriate to cloth-like objects. In addition, further refinements to the object’s manipulation type were proposed. In another survey, on the multi-robot manipulation of deformable objects, Herguedas et al. (<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2019" title="Herguedas, R., Lopez-Nicolas, G., Aragues, R., & Sagues, C. (2019). Survey on multi-robot manipulation of deformable objects. In 2019 24th IEEE International conference on emerging technologies and factory automation (ETFA) (pp. 977–984). 
 https://doi.org/10.1109/etfa.2019.8868987
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR21" id="ref-link-section-d64739081e486">2019</a>) reviewed different research perspectives covering object geometry, object manipulation type, and robotic system components. In all the above reviews, object attributes constituted the main classification axis, with manipulation type typically supporting further divisions. However, such classifications are limited in terms of evaluating synergies between the robotics and the manufacturing process which are critical for processes with multiple manipulation stages and the integration of robotic processes (e.g., assembly) into factory floor operations.</p><p>For robots to successfully perform required assembly operation, dexterous manipulation and intricate collaboration with additional production system components (e.g., multiple robotic arms, end-effectors, designated jigs) are typically required. Developing required operations and programming the collaboration between production agents is challenging and time-consuming. Machine learning methods, such as deep reinforcement learning used today for learning complex robotic behaviour, typically require lengthy learning periods and costly computation facilities (Panzer & Bender, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2021" title="Panzer, M., & Bender, B. (2021). Deep reinforcement learning in production systems: A systematic literature review. International Journal of Production Research, 60(13), 4316–4341. 
 https://doi.org/10.1080/00207543.2021.1973138
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR49" id="ref-link-section-d64739081e492">2021</a>). The prolonged time and the high costs limit the ability to use such advanced deep learning methods in flexible manufacturing environments. The limitations are especially problematic in small batch-size production facilities, where fast setup times and low planning costs are crucial for competitiveness (Carvajal Soto et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2019" title="Carvajal Soto, J. A., Tavakolizadeh, F., & Gyulai, D. (2019). An online machine learning framework for early detection of product failures in an industry 4.0 context. International Journal of Computer Integrated Manufacturing, 32(4–5), 452–465. 
 https://doi.org/10.1080/0951192x.2019.1571238
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR13" id="ref-link-section-d64739081e495">2019</a>; Usuga Cadavid et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2020" title="Usuga Cadavid, J. P., Lamouri, S., Grabot, B., Pellerin, R., & Fortin, A. (2020). Machine learning applied in production planning and control: A state-of-the-art in the era of industry 4.0. Journal of Intelligent Manufacturing, 31(6), 1531–1558. 
 https://doi.org/10.1007/s10845-019-01531-7
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR64" id="ref-link-section-d64739081e498">2020</a>).</p><p>Manufacturing setup times and planning costs can be reduced by operation reuse and end-to-end automation of planning and execution. End-to-end planning and execution methods are typically divided into three modules: data extraction, process planning, and process execution (Wang et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2008" title="Wang, L., Keshavarzmanesh, S., Feng, H. Y., & Buchal, R. O. (2008). Assembly process planning and its future in Collaborative Manufacturing: A review. The International Journal of Advanced Manufacturing Technology, 41(1–2), 132–144. 
 https://doi.org/10.1007/s00170-008-1458-9
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR69" id="ref-link-section-d64739081e504">2008</a>; Yang et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2013" title="Yang, Q., Wu, D. L., Zhu, H. M., Bao, J. S., & Wei, Z. H. (2013). Assembly operation process planning by mapping a virtual assembly simulation to real operation. Computers in Industry, 64(7), 869–879. 
 https://doi.org/10.1016/j.compind.2013.06.001
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR74" id="ref-link-section-d64739081e507">2013</a>). Despite advances in automated manufacturing, there remains a critical gap in the development of a comprehensive framework suitable for dexterous assemblies with deformable objects that integrate data extraction from design documents, robotic sequence planning, and real-time execution control. Particularly a framework that can demonstrate robust performance in various industrial manufacturing environments with commercial products. The current work suggests an end-to-end framework for automatic planning and execution within a centralized database based on a holistic robotic manufacturing ontology (described in “<a data-track="click" data-track-label="link" data-track-action="section anchor" href="/article/10.1007/s10845-025-02578-5#Sec9">Rαβγ database</a>” section) for assembly with deformable objects, which enables reusability of assembly operations. The framework addresses challenges in all three framework modules.</p><p>Data extraction from design documents is required to identify points of interest (e.g., peg-in-hole locations) and the characteristics of the parts influencing the assembly process (e.g., deformation type). Computer-aided design (CAD) models are a widespread documentation form of geometric information, typically used for documenting the assembly of rigid objects, i.e., objects with negligible deformation (Koga et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2022" title="Koga, Y., Kerrick, H., & Chitta, S. (2022). On CAD informed Adaptive Robotic Assembly. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 
 https://doi.org/10.1109/iros47612.2022.9982242
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR29" id="ref-link-section-d64739081e517">2022</a>; Nikolov et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2023" title="Nikolov, G. N., Thomsen, A. N., Mikkelstrup, A. F., & Kristiansen, M. (2023). Computer-aided process planning system for laser forming: From CAD to part. International Journal of Production Research. 
 https://doi.org/10.1080/00207543.2023.2241565
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR45" id="ref-link-section-d64739081e520">2023</a>). Assemblies with deformable objects are typically documented using additional documents, e.g., job guides and data sheets (Shneor & Berman, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2023a" title="Shneor, R., & Berman, S. (2023a). Towards production planning automation: mapping documents for robotic assembly planning with DLO. In The 27th International conference on production research, Cluj-Napoca, Romania, 23–28 July 2023." href="/article/10.1007/s10845-025-02578-5#ref-CR57" id="ref-link-section-d64739081e523">2023a</a>). In many cases, the available documentation may not include a CAD module. While CAD documents are structured and have a standardized format (such as STEP), the documents typically used for documenting assembly with deformable objects lack standardization. The current work takes a data-driven approach, integrating data from CAD models and additional manufacturing documents with user interfaces to accommodate manufacturing documents that lack standardization.</p><p>Process planning frameworks for rigid object assembly concentrate on constraints related to the end state of the assembled parts. Due to possible deformations during interaction and mating, assembly planning with deformable objects must consider the dynamic constraints during the entire object manipulation operation rather than solely adhering to constraints related to the end state (Zhu et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2022" title="Zhu, J., Cherubini, A., Dune, C., Navarro-Alarcon, D., Alambeigi, F., Berenson, D., Ficuciello, F., Harada, K., Kober, J., Li, X., Pan, J., Yuan, W., & Gienger, M. (2022). Challenges and outlook in robotic manipulation of deformable objects. IEEE Robotics & Automation Magazine, 29(3), 67–77. 
 https://doi.org/10.1109/mra.2022.3147415
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR78" id="ref-link-section-d64739081e529">2022</a>). This requirement complicates motion planning and collaboration between manufacturing agents during execution.</p><p>A commonly used division in robotic process programming is low-level robotic skills and high-level semantic operations (Liang et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2019" title="Liang, Y. S., Pellier, D., Fiorino, H., & Pesty, S. (2019). End-user programming of low-and high-level actions for Robotic Task Planning. In 2019 28th IEEE international conference on robot and human interactive communication (RO-MAN). 
 https://doi.org/10.1109/ro-man46459.2019.8956327
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR34" id="ref-link-section-d64739081e535">2019</a>). Skills encode robotic motion profiles (profile encoding may include any combination of path, speed, and acceleration definitions) and low-level operations, e.g., open gripper, close gripper. Semantic operations encode process-level operations, e.g., insert wire, glue tube. For deformable object assembly, collaboration between several agents, where each agent executes several skills, may be required to perform each assembly operation. The current work builds on parametrized hybrid automata models to support such a collaboration.</p><p>Process execution streamlines the manufacturing procedures to assemble the desired final product. Industrial process execution requires fine-tuning robotic motion planning to the capabilities of the executing robot and adaptation to environment uncertainties, e.g., part dimension or workstation setup tolerances. In assembly with deformable objects, the need for motion adaptation during assembly is fundamental due to the inherent uncertainty caused by the deformable characteristics of the assembled parts. These operations should undergo validation and testing in simulation before actual production. To support motion adaptation and validation, the current work developed an interface for real-time interaction with the process execution module, integrating a digital twin and a physical production setup.</p><p>The framework presented was developed for robotic work cells using a data-driven approach. The data-driven approach relies on different information sources that are parametrized and stored in the database (Wang et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2011" title="Wang, J., Chang, Q., Xiao, G., Wang, N., & Li, S. (2011). Data driven production modeling and simulation of Complex Automobile General Assembly Plant. Computers in Industry, 62(7), 765–775. 
 https://doi.org/10.1016/j.compind.2011.05.004
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR70" id="ref-link-section-d64739081e544">2011</a>). Parametrizing data into a database simplifies process planning with multiple entities and complex data structures and facilitates process abstraction, enabling skill and process reuse. The current work implements a data-driven approach for robotic assembly planning and execution of commercial products with deformable objects. The contributions and innovations of the current work are described below:</p> <ul class="u-list-style-bullet"> <li> <p>A data-driven approach using a central database that enables central storage of the information needed for data extraction, process planning, and process execution modules. The central database is the main pivot interconnecting the information required for the framework’s modules and their relevant parameters. The parameters and data extracted from multiple data sources regarding deformable objects are stored in the database. The central database includes the relevant information for the robotic assembly of deformable objects, thus contributing to uniformity and consistency in planning and execution.</p> </li> <li> <p>The hybrid automata model encompasses process and collaboration complexity using two layers: assembly operations and robotic skills. The parametrized hybrid automata of robotic skills linked to assembly operations support dexterous operation planning and execution. The parametrization enables the use of high-level assembly operations for dexterous operations by adjusting the values of the robotic skills and efficiently sequencing assembly operations. The parameterization of low-level robotic skills linked to high-level assembly operations reduces manufacturing times and costs by enhancing the reusability of pre-planned and stored assembly operations. Facilitating the reusability of assembly operations contributes to shortening engineering time for planning and execution (e.g., setup times between products), which is important, especially when addressing wiring activities that are considered hard to automate and digitize (Bründl et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2024" title="Bründl, P., Stoidner, M., Bredthauer, J., Nguyen, H. G., Baechler, A., & Franke, J. (2024). Unlocking the potential of digitalization and automation: A qualitative and quantitative study of the Control Cabinet Manufacturing Industry. Production & Manufacturing Research. 
 https://doi.org/10.1080/21693277.2024.2306820
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR10" id="ref-link-section-d64739081e562">2024</a>).</p> </li> <li> <p>Extracting information from multiple data sources of real-world products (e.g., a CAD model for part assembly and object locations, a datasheet for part properties and connections indexes, and an electric scheme for wiring plans) contributes to the concretization of the developed framework. Integrating such sources and data types, together with process planning based on hybrid automata model parametrization for robotic configurations and process execution based on a digital twin, is an additional contribution of this work.</p> </li> <li> <p>The developed framework was demonstrated with different assembly processes of real-world products from different industries. The framework suggested is innovative in its generic structure and reusable building blocks (assembly operations) and facilitates handling various interactions with deformable objects (rigid and deformable, deformable and deformable).</p> </li> </ul><p>The rest of this paper is organized as follows: the second section describes related work; “<a data-track="click" data-track-label="link" data-track-action="section anchor" href="/article/10.1007/s10845-025-02578-5#Sec8">Method</a>” section details the framework and modules; “<a data-track="click" data-track-label="link" data-track-action="section anchor" href="/article/10.1007/s10845-025-02578-5#Sec4">Case study</a>” section presents a case study of the framework with two industry types, electrical (smart light boards) and a medical (infusion kit); the fifth section presents a discussion; and the final section conclusions.</p></div></div></section><section data-title="Related work"><div class="c-article-section" id="Sec2-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Sec2">Related work</h2><div class="c-article-section__content" id="Sec2-content"><p>Related work is reviewed based on the three modules of planning and execution automation frameworks: data extraction, process planning, and process execution. In the current work, a central database interconnects all three models. The baseline for formally constructing the database is additionally reviewed as well as recent robotic planning and execution frameworks.</p><h3 class="c-article__sub-heading" id="Sec3">Data extraction</h3><p>Automatically extracting manufacturing data from design documents is an initial and important step in manufacturing process automation and its application in intelligent manufacturing. Extracting manufacturing data is challenging due to the heterogeneous multi-sources and diverse locations of manufacturing documents in different systems. In addition, semi-structured characteristics of manufacturing sources are part of the inherent complexity of manufacturing information structure (Ren et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2022" title="Ren, L., Li, Y., Wang, X., Cui, J., & Zhang, L. (2022). An abge-aided manufacturing knowledge graph construction approach for heterogeneous IIOT data integration. International Journal of Production Research, 61(12), 4102–4116. 
 https://doi.org/10.1080/00207543.2022.2042416
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR51" id="ref-link-section-d64739081e604">2022</a>).</p><p>For robotic assembly, the primary source for data extraction is typically CAD models, where there are widespread formats for the geometric representation of objects (Neb, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2019" title="Neb, A. (2019). Review on approaches to generate assembly sequences by extraction of Assembly features from 3D models. Procedia CIRP, 81, 856–861. 
 https://doi.org/10.1016/j.procir.2019.03.213
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR43" id="ref-link-section-d64739081e610">2019</a>; Thomas et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2018" title="Thomas, G., Chien, M., Tamar, A., Ojea, J. A., & Abbeel, P. (2018). Learning Robotic Assembly from CAD. 2018 IEEE International Conference on Robotics and Automation (ICRA). 
 https://doi.org/10.1109/icra.2018.8460696
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR61" id="ref-link-section-d64739081e613">2018</a>). CAD models are based on several common formats (e.g., STEP or IGES). There are CAD models at the part level and CAD models of an assembly, i.e., combining several parts into a product. The end condition of parts in an assembly can be identified in a CAD model and used to determine assembly planning constraints (Pane et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2020" title="Pane, Y., Arbo, M. H., Aertbelien, E., & Decre, W. (2020). A system architecture for CAD-based Robotic Assembly with sensor-based skills. IEEE Transactions on Automation Science and Engineering. 
 https://doi.org/10.1109/tase.2020.2980628
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR48" id="ref-link-section-d64739081e616">2020</a>).</p><p>CAD models do not encode deformable object characteristics (Fakhurldeen et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2019" title="Fakhurldeen, H., Dailami, F., & Pipe, A. G. (2019). Cara system architecture—a click and assemble robotic assembly system. In 2019 International conference on robotics and automation (ICRA). 
 https://doi.org/10.1109/icra.2019.8794114
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR16" id="ref-link-section-d64739081e622">2019</a>; Tariki et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2020" title="Tariki, K., Kiyokawa, T., Nagatani, T., Takamatsu, J., & Ogasawara, T. (2020). Generating complex assembly sequences from 3D CAD models considering insertion relations. Advanced Robotics, 35(6), 337–348. 
 https://doi.org/10.1080/01691864.2020.1863258
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR60" id="ref-link-section-d64739081e625">2020</a>). Therefore, information from additional documents must be incorporated for assembly with deformable objects. These include datasheets that describe part characteristics, e.g., degree of deformation and electrical ports indexes; Electrical schemes that describe required electrical wiring (for electrical parts); The bill of materials which describes the assembled materials (which may not be detailed in the CAD model); A job guide which textually describes how the assembly process is conducted. Unlike datasheets and CAD models, freely available on manufacturers’ or vendors’ websites, job guides are considered company-proprietary and are less accessible documents (Shneor & Berman, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2023a" title="Shneor, R., & Berman, S. (2023a). Towards production planning automation: mapping documents for robotic assembly planning with DLO. In The 27th International conference on production research, Cluj-Napoca, Romania, 23–28 July 2023." href="/article/10.1007/s10845-025-02578-5#ref-CR57" id="ref-link-section-d64739081e628">2023a</a>).</p><h3 class="c-article__sub-heading" id="Sec4">Process planning</h3><p>Planning collaboration between agents and reusing and adapting previously learned robotic skills can be facilitated by modelling the production process using hybrid automata models (Lee & Seshia, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2017" title="Lee, E. A., & Seshia, S. A. (2017). Introduction to embedded systems—a cyber-physical systems approach (2nd ed.). MIT." href="/article/10.1007/s10845-025-02578-5#ref-CR31" id="ref-link-section-d64739081e639">2017</a>). A hybrid automata model is a finite state machine with continuous variables whose values are described by a set of ordinary differential equations. Hybrid automata models are suitable for describing systems, e.g., robotic assembly systems, with timed and event-based transitions between continuous behaviours (Zhao et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2023" title="Zhao, X., Zheng, L., Shi, M., Zhang, X., & Zhang, Y. (2023). Unified modelling for continuous–discrete hybrid adaptive machining CPS of large thin-walled parts. International Journal of Production Research. 
 https://doi.org/10.1080/00207543.2023.2217304
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR77" id="ref-link-section-d64739081e642">2023</a>). Models can be combined serially or in parallel, synchronously, or asynchronously to construct the collaboration between agents, i.e., active production equipment, such as a robot and a controlled gripper. The hybrid automata models also facilitate the use of parameters for adding a level of generalization to the modelled systems (Southier et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2022" title="Southier, L. F., Casanova, D., Barbosa, L., Torrico, C., Barbosa, M., & Teixeira, M. (2022). Modelling and control of manufacturing systems subject to context recognition and switching. International Journal of Production Research, 61(10), 3396–3414. 
 https://doi.org/10.1080/00207543.2022.2081631
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR59" id="ref-link-section-d64739081e645">2022</a>). By replacing the ordinary differential equations model, deep neural networks can be seamlessly integrated with hybrid automata-based control architectures (e.g., through reinforcement learning methods). This capability is a major advantage of hybrid-automata models since deep neural networks are now commonly used for modelling and controlling complex continuous behaviours (Ying et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2021" title="Ying, K. C., Pourhejazy, P., Cheng, C. Y., & Cai, Z. Y. (2021). Deep learning-based optimization for motion planning of dual-ARM assembly robots. Computers & Industrial Engineering, 160, 107603. 
 https://doi.org/10.1016/j.cie.2021.107603
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR75" id="ref-link-section-d64739081e648">2021</a>). An additional advantage of the use of hybrid automata is that it facilitates testing system behaviour at the modelling stage (Lee & Seshia, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2017" title="Lee, E. A., & Seshia, S. A. (2017). Introduction to embedded systems—a cyber-physical systems approach (2nd ed.). MIT." href="/article/10.1007/s10845-025-02578-5#ref-CR31" id="ref-link-section-d64739081e651">2017</a>).</p><p>An example of a complex assembly operation suitable for deep neural network hybrid automata is the peg-in-hole assembly, which is a widespread industrial operation. Robotic peg-in-hole assembly is complex due to force feedback, mechanical impedance, and compliance challenges stemming from setup or part dimension uncertainties. The peg-in-hole operation is even more complicated when deformable objects are involved. Deep reinforcement learning was used to learn an impedance controller for inserting a peg with plastic deformation based on a force and tactile sensor (Kozlovsky et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2022" title="Kozlovsky, S., Newman, E., & Zacksenhouse, M. (2022). Reinforcement learning of impedance policies for peg-in-hole tasks: Role of asymmetric matrices. IEEE Robotics and Automation Letters, 7(4), 10898–10905. 
 https://doi.org/10.1109/lra.2022.3191070
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR30" id="ref-link-section-d64739081e657">2022</a>). Such a learned controller can form a skill and be integrated within a hybrid automata model for robotic peg-in-hole insertion. The model may include additional skills for the initial peg positioning based on an image sensor and integration with a gripper agent with opening and closing skills.</p><p>The assembly sequence planning is a non-polynomial hard problem. Soft computing methods, e.g., genetic algorithms, are commonly used for assembly sequence planning (Abdullah et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2018" title="Abdullah, M. A., Rashid, A., M. F., & Ghazalli, Z. (2018). Optimization of assembly sequence planning using soft computing approaches: A review. Archives of Computational Methods in Engineering, 26(2), 461–474. 
 https://doi.org/10.1007/s11831-018-9250-y
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR1" id="ref-link-section-d64739081e663">2018</a>; Cancino et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2017" title="Cancino, C., Merigó, J. M., Coronado, F., Dessouky, Y., & Dessouky, M. (2017). Forty years of computers & industrial engineering: A bibliometric analysis. Computers & Industrial Engineering, 113, 614–629. 
 https://doi.org/10.1016/j.cie.2017.08.033
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR12" id="ref-link-section-d64739081e666">2017</a>; Jiménez, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2011" title="Jiménez, P. (2011). Survey on assembly sequencing: A combinatorial and geometrical perspective. Journal of Intelligent Manufacturing, 24(2), 235–250. 
 https://doi.org/10.1007/s10845-011-0578-5
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR26" id="ref-link-section-d64739081e669">2011</a>; Neumann et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2023" title="Neumann, A., Hajji, A., Rekik, M., & Pellerin, R. (2023). Genetic algorithms for planning and scheduling engineer-to-order production: A systematic review. International Journal of Production Research. 
 https://doi.org/10.1080/00207543.2023.2237122
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR44" id="ref-link-section-d64739081e672">2023</a>). Many studies have been conducted on automatic robotic assembly planning for rigid object assembly (Fakhurldeen et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2019" title="Fakhurldeen, H., Dailami, F., & Pipe, A. G. (2019). Cara system architecture—a click and assemble robotic assembly system. In 2019 International conference on robotics and automation (ICRA). 
 https://doi.org/10.1109/icra.2019.8794114
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR16" id="ref-link-section-d64739081e675">2019</a>; Rodríguez et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2020" title="Rodríguez, I., Nottensteiner, K., Leidner, D., Durner, M., Stulp, F., & Albu-Schaffer, A. (2020). Pattern recognition for knowledge transfer in Robotic Assembly sequence planning. IEEE Robotics and Automation Letters, 5(2), 3666–3673. 
 https://doi.org/10.1109/lra.2020.2979622
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR52" id="ref-link-section-d64739081e679">2020</a>; Tariki et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2020" title="Tariki, K., Kiyokawa, T., Nagatani, T., Takamatsu, J., & Ogasawara, T. (2020). Generating complex assembly sequences from 3D CAD models considering insertion relations. Advanced Robotics, 35(6), 337–348. 
 https://doi.org/10.1080/01691864.2020.1863258
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR60" id="ref-link-section-d64739081e682">2020</a>).</p><p>Deformable objects require intricate automatic assembly planning and execution due to low documentation standardization, potential object deformations, and the need for collaboration between manufacturing agents. Intricate planning is especially crucial for robotic production with minor human involvement on the production floor. A limited number of research projects on assembly sequence planning consider the challenges of assembly with deformable objects. However, products with deformable objects are common in real-world industrial assemblies. For example, deformable linear objects, e.g., wires for electrical conductivity, gaskets for sealing, or infusion tubes for fluid flow. Notable work on assembly with deformable objects includes contact state representation, e.g., vertex to face, edge to vertex, and transition between contact states (Acker & Henrich, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2005" title="Acker, J., & Henrich, D. (2005). Manipulation of deformable linear objects: From geometric model towards program generation. In Proceedings of the 2005 IEEE international conference on robotics and automation. 
 https://doi.org/10.1109/robot.2005.1570333
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR2" id="ref-link-section-d64739081e688">2005</a>). Another work applied greedy heuristics based on the geometric and mechanical properties of the parts (e.g., precedence relations) for determining the assembly sequence planning containing deformable objects (Masehian & Ghandi, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2021" title="Masehian, E., & Ghandi, S. (2021). Assembly sequence and path planning for monotone and nonmonotone assemblies with rigid and flexible parts. Robotics and Computer-Integrated Manufacturing, 72, 102180. 
 https://doi.org/10.1016/j.rcim.2021.102180
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR39" id="ref-link-section-d64739081e691">2021</a>). However, an end-to-end framework integrating data extraction from design documents, robotic assembly planning, and runtime execution, suitable for shop floor environments and real-world products is lacking.</p><h3 class="c-article__sub-heading" id="Sec5">Process execution</h3><p>Digital twins facilitate a virtual representation of a physical setup with the ability to perform motion planning, simulation-based predictions, stochastic dynamic evaluations, testing, and monitoring of robotic process sequences (Liu et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2023" title="Liu, S., Zheng, P., & Bao, J. (2023). Digital twin-based manufacturing system: A survey based on a novel reference model. Journal of Intelligent Manufacturing, 35(6), 2517–2546. 
 https://doi.org/10.1007/s10845-023-02172-7
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR36" id="ref-link-section-d64739081e702">2023</a>; Luo et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2022" title="Luo, D., Thevenin, S., & Dolgui, A. (2022). A state-of-the-art on Production Planning in Industry 4.0. International Journal of Production Research, 61(19), 6602–6632. 
 https://doi.org/10.1080/00207543.2022.2122622
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR38" id="ref-link-section-d64739081e705">2022</a>; Overbeck et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2023" title="Overbeck, L., Graves, S. C., & Lanza, G. (2023). Development and analysis of Digital Twins of Production systems. International Journal of Production Research. 
 https://doi.org/10.1080/00207543.2023.2242525
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR46" id="ref-link-section-d64739081e708">2023</a>). Digital twins have been applied for multiple domains, including fault diagnosis, sheet metal forming, supply chain, energy optimization, and operation management (Ivanov, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2023" title="Ivanov, D. (2023). Conceptualisation of a 7-element digital twin framework in supply chain and Operations Management. International Journal of Production Research. 
 https://doi.org/10.1080/00207543.2023.2217291
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR22" id="ref-link-section-d64739081e711">2023</a>; Modad et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2024" title="Modad, O. A., Ryska, J., Chehade, A., & Ayoub, G. (2024). Revolutionizing sheet metal stamping through industry 5.0 Digital Twins: A comprehensive review. Journal of Intelligent Manufacturing. 
 https://doi.org/10.1007/s10845-024-02453-9
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR41" id="ref-link-section-d64739081e714">2024</a>; Xia et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2025" title="Xia, T., Sun, H., Ding, Y., Han, D., Qin, W., Seidelmann, J., & Xi, L. (2025). Digital twin-based real-time energy optimization method for production line considering fault disturbances. Journal of Intelligent Manufacturing, 36(1), 569–593. 
 https://doi.org/10.1007/s10845-023-02219-9
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR73" id="ref-link-section-d64739081e718">2025</a>; Zhang et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2024" title="Zhang, H., Wang, Z., Zhang, S., Qiu, L., Wang, Y., Xiang, F., Pan, Z., Zhu, L., & Tan, J. (2024). Digital-triplet: A new three entities digital-twin paradigm for Equipment Fault diagnosis. Journal of Intelligent Manufacturing. 
 https://doi.org/10.1007/s10845-024-02471-7
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR76" id="ref-link-section-d64739081e721">2024</a>). The detailed motion planning capabilities of digital twins facilitate transforming paths planned as waypoints to dynamic robot trajectories for the specific robot module deployed and motion adaptation during process execution (Böttjer et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2023" title="Böttjer, T., Tola, D., Kakavandi, F., Wewer, C. R., Ramanujan, D., Gomes, C., Larsen, P. G., & Iosifidis, A. (2023). A review of unit level digital twin applications in the manufacturing industry. CIRP Journal of Manufacturing Science and Technology, 45, 162–189. 
 https://doi.org/10.1016/j.cirpj.2023.06.011
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR7" id="ref-link-section-d64739081e724">2023</a>; Overbeck et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2023" title="Overbeck, L., Graves, S. C., & Lanza, G. (2023). Development and analysis of Digital Twins of Production systems. International Journal of Production Research. 
 https://doi.org/10.1080/00207543.2023.2242525
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR46" id="ref-link-section-d64739081e727">2023</a>). Moreover, executing a robotic operation sequence with a digital twin before the physical process execution validates the sequence and ensures successful operation on a real-world shop floor during run-time.</p><p>Research efforts for developing industrial applications of robotic assembly of real-world products containing deformable objects concentrate on developing customized robot work cells for specific applications. For example, robotic systems with tactile and vision sensors and dedicated end-effectors were developed in the footwear industry, focusing on collaborative robots (Mezouar, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2021" title="Mezouar, Y. (2021). COMMANDIA (Collaborative RObotic Mobile MANipulation of Deformable objects in Industrial Applications). 
 http://commandia.unizar.es/
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR40" id="ref-link-section-d64739081e733">2021</a>) and in the electronics industry for cabling and wiring (Palli et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2019" title="Palli, G., Pirozzi, S., Indovini, M., De Gregorio, D., Zanella, R., & Melchiorri, C. (2019). Automatized switchgear wiring: An outline of the wires experiment results. In Springer tracts in advanced robotics (pp. 107–123). Springer. 
 https://doi.org/10.1007/978-3-030-22327-4_6
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR47" id="ref-link-section-d64739081e736">2019</a>). Existing commercial solutions for robotic wiring require relatively large space between parts, fixed orientation during assembly, and a low number of wires (Busi et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2017" title="Busi, M., Cirillo, A., De Gregorio, D., Indovini, M., De Maria, G., Melchiorri, C., Natale, C., Palli, G., & Pirozzi, S. (2017). The wires experiment: Tools and strategies for robotized switchgear cabling. Procedia Manufacturing, 11, 355–363. 
 https://doi.org/10.1016/j.promfg.2017.07.118
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR11" id="ref-link-section-d64739081e739">2017</a>).</p><h3 class="c-article__sub-heading" id="Sec6">Database</h3><p>In line with the data-driven concept, database structure is a central framework aspect since the stored data drives both process planning and execution. Constructing the database based on an established robotic process categorization is important to facilitate a comprehensive, coherent representation that is relevant to real-world production environments.</p><p>Classical categorizations of robotic hardware are based on robot characteristics and characteristics of various system elements, e.g., grippers, actuators, and sensors (Hazarika & Dixit, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2018" title="Hazarika, S. M., & Dixit, U. S. (2018). Robotics: History, trends, and future directions. In Introduction to mechanical engineering. Springer. 
 https://doi.org/10.1007/978-3-319-78488-5_7
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR20" id="ref-link-section-d64739081e754">2018</a>). Categorization was also specifically suggested for robotic manipulation of deformable objects, including attributes such as deformable object geometry (e.g., linear, surface, volumetric) or activity type (e.g., inserting, folding) (Jiménez, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2012" title="Jiménez, P. (2012). Survey on model-based manipulation planning of deformable objects. Robotics and Computer-Integrated Manufacturing, 28(2), 154–163. 
 https://doi.org/10.1016/j.rcim.2011.08.002
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR27" id="ref-link-section-d64739081e757">2012</a>; Sanchez et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2018" title="Sanchez, J., Corrales, J. A., Bouzgarrou, B. C., & Mezouar, Y. (2018). Robotic manipulation and sensing of deformable objects in domestic and industrial applications: A survey. The International Journal of Robotics Research, 37(7), 688–716. 
 https://doi.org/10.1177/0278364918779698
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR54" id="ref-link-section-d64739081e760">2018</a>; Trommnau et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2020" title="Trommnau, J., Frommknecht, A., Siegert, J., Wößner, J., & Bauernhansl, T. (2020). Design for Automatic Assembly: A new approach to classify limp components. Procedia CIRP, 91, 49–54. 
 https://doi.org/10.1016/j.procir.2020.01.136
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR63" id="ref-link-section-d64739081e763">2020</a>). However, for constructing a database that can encapsulate interactions between different process components, it is important that the categorization holistically considers all process components, including hardware, software, and processes-related attributes.</p><p>The Rαβγ categorization framework (Shneor & Berman, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2022a" title="Shneor, R., & Berman, S. (2022a). The Rαβγ categorisation framework for dexterous robotic Manufacturing processes. International Journal of Production Research. 
 https://doi.org/10.1080/00207543.2022.2150907
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR55" id="ref-link-section-d64739081e769">2022a</a>) is a holistic categorization for robotic manufacturing processes based on the classical α|β|γ scheduling classification (Graham et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 1979" title="Graham, R. L., Lawler, E. L., Lenstra, J. K., & Kan, A. H. G. R. (1979). Optimization and approximation in deterministic sequencing and scheduling: A survey. Annals of Discrete Mathematics. 
 https://doi.org/10.1016/s0167-5060(08)70356-x
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR19" id="ref-link-section-d64739081e772">1979</a>). The α|β|γ classification is widely applied to classify scheduling problems (Kayhan & Yildiz, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2021" title="Kayhan, B. M., & Yildiz, G. (2021). Reinforcement learning applications to machine scheduling problems: A comprehensive literature review. Journal of Intelligent Manufacturing, 34(3), 905–929. 
 https://doi.org/10.1007/s10845-021-01847-3
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR28" id="ref-link-section-d64739081e775">2021</a>; Wocker et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2023" title="Wocker, M. M., Ostermeier, F. F., Wanninger, T., Zwinkau, R., & Deuse, J. (2023). Flexible job shop scheduling with preventive maintenance consideration. Journal of Intelligent Manufacturing, 35(4), 1517–1539. 
 https://doi.org/10.1007/s10845-023-02114-3
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR72" id="ref-link-section-d64739081e778">2023</a>). Furthermore, the α|β|γ has been adopted for a range of additional fields, including genetic algorithms (Akgündüz & Tunalı, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2011" title="Akgündüz, O. S., & Tunalı, S. (2011). A review of the current applications of genetic algorithms in mixed-model assembly line sequencing. International Journal of Production Research, 49(15), 4483–4503. 
 https://doi.org/10.1080/00207543.2010.495085
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR3" id="ref-link-section-d64739081e781">2011</a>), sports timetabling (Van Bulck et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2020" title="Van Bulck, D., Goossens, D., Schönberger, J., & Guajardo, M. (2020). Robinx: A three-field classification and unified data format for round-robin sports timetabling. European Journal of Operational Research, 280(2), 568–580. 
 https://doi.org/10.1016/j.ejor.2019.07.023
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR67" id="ref-link-section-d64739081e785">2020</a>), and sharing economy (Boysen et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2019" title="Boysen, N., Briskorn, D., & Schwerdfeger, S. (2019). Matching supply and demand in a sharing economy: Classification, computational complexity, and application. European Journal of Operational Research, 278(2), 578–595. 
 https://doi.org/10.1016/j.ejor.2019.04.032
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR8" id="ref-link-section-d64739081e788">2019</a>). The Rαβγ categorization builds upon the α|β|γ three-field nomenclature with additional constructs from robotics, manufacturing, and deformable objects domains. The integration of manufacturing resources, process tasks, and measurements offered by the Rαβγ offers a structured data-based view that facilitates the automation of assembly planning based on process building blocks. Rαβγ establishes a taxonomy through a three-tiered structure, where tiers are characterized based on both qualitative meta-parameters and quantifiable characteristics. The following meta-parameters were applied as part of the Rαβγ: environment, industry, and measurements. The environment is where the work cell components (material and actor) operate. The environment is an important factor for understanding implementation’s implications, and for evaluating implementation feasibility, as the translation of research findings from controlled environment conditions (e.g., laboratory) to industrial manufacturing environments is challenging. The industry classification of industrial sectors facilitates the detection and analysis of domain-specific patterns, characterized by distinct operational requirements and specialized workflows. The measurements are essential for precision and reliability and enable analysis and improvement.</p><p>The Rαβγ categorization was demonstrated in descriptive scenarios such as shipbuilding welding or robotic competitions (Shneor & Berman, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2022a" title="Shneor, R., & Berman, S. (2022a). The Rαβγ categorisation framework for dexterous robotic Manufacturing processes. International Journal of Production Research. 
 https://doi.org/10.1080/00207543.2022.2150907
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR55" id="ref-link-section-d64739081e794">2022a</a>). The Rαβγ categorization can be used for defining different strategies to address uncertainties related to robotic manufacturing with deformable objects. Such strategies can be divided into three categories: reducing process uncertainty (e.g., by designated fixtures or dedicated robotic arms), reducing the susceptibility of the manufacturing system to uncertainty (e.g., by accurate simulation or advanced sequence planning), and perceiving and reacting to the dynamic changes related to the uncertainty (e.g., by advanced tactile sensors or perception based on vision sensors) (Shneor & Berman, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2023b" title="Shneor, R., & Berman, S. (2023b). Robotic Assembly with deformable objects. In Systems collaboration and integration (pp. 221–235). Springer. 
 https://doi.org/10.1007/978-3-031-44373-2_13
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR58" id="ref-link-section-d64739081e797">2023b</a>).</p><h3 class="c-article__sub-heading" id="Sec7">Robotic planning and executing frameworks</h3><p>Planning and execution frameworks developed to date (Table <a data-track="click" data-track-label="link" data-track-action="table anchor" href="/article/10.1007/s10845-025-02578-5#Tab1">1</a>) mainly target rigid object assembly in controlled environments (e.g., laboratory or simulation), typically with a single measure, without emphasis on assembly operation reuse, and with decentralized data storage for the assembly planning and execution modules. The indicators described in Table <a data-track="click" data-track-label="link" data-track-action="table anchor" href="/article/10.1007/s10845-025-02578-5#Tab1">1</a> are based on the meta-parameters of the Rαβγ categorization for robotic manufacturing processes (i.e., environment, industry, and measurement). Rodríguez et al. (<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2020" title="Rodríguez, I., Nottensteiner, K., Leidner, D., Durner, M., Stulp, F., & Albu-Schaffer, A. (2020). Pattern recognition for knowledge transfer in Robotic Assembly sequence planning. IEEE Robotics and Automation Letters, 5(2), 3666–3673. 
 https://doi.org/10.1109/lra.2020.2979622
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR52" id="ref-link-section-d64739081e814">2020</a>) applied knowledge transfer for the robotic assembly sequence planner (KT-RASP) method using graph topology of parts, surfaces, and instances for comparison of a new product assembly to known topologies and deducing assembly rules and constraints. The method was demonstrated on a simple part (containing 3 perpendicular profiles and 2 angle brackets) with three-dimensional (3D) rigid parts. Fakhurldeen et al. (<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2019" title="Fakhurldeen, H., Dailami, F., & Pipe, A. G. (2019). Cara system architecture—a click and assemble robotic assembly system. In 2019 International conference on robotics and automation (ICRA). 
 https://doi.org/10.1109/icra.2019.8794114
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR16" id="ref-link-section-d64739081e817">2019</a>) suggested a click-and-assemble robotic assembly (CARA) system by first generating assembly sequence plans, then producing a robotic assembly plan, and executing it. A CAD file input was disassembled to different parts and reversed to determine assembly sequences, i.e., assembly by disassembly. The technical demonstration was conducted with two products containing 4 parts and 2 subassemblies all with rigid parts. Tariki et al. (<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2020" title="Tariki, K., Kiyokawa, T., Nagatani, T., Takamatsu, J., & Ogasawara, T. (2020). Generating complex assembly sequences from 3D CAD models considering insertion relations. Advanced Robotics, 35(6), 337–348. 
 https://doi.org/10.1080/01691864.2020.1863258
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR60" id="ref-link-section-d64739081e820">2020</a>) defined insertionable properties of parts for robotic assembly planning. The authors propose an insertion matrix derived from the assembly model, representing female and male parts, i.e., inserted parts and parts that are inserted into, respectively, in a product. They use a genetic algorithm to determine the assembly sequence plans and demonstrated it with three products containing 4, 5, and 32 3D rigid parts. Pane et al. (<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2020" title="Pane, Y., Arbo, M. H., Aertbelien, E., & Decre, W. (2020). A system architecture for CAD-based Robotic Assembly with sensor-based skills. IEEE Transactions on Automation Science and Engineering. 
 https://doi.org/10.1109/tase.2020.2980628
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR48" id="ref-link-section-d64739081e824">2020</a>) suggest a three-layered system with separated databases for each layer for robotic assembly using CAD models to generate executable robot skills. The system maps CAD-level assembly specifications to executable robot skills by reasoning about the relationships between geometric primitives, assembly constraints, and robot motion constraints represented in a knowledge database structured as an ontology. The knowledge is used to infer the appropriate skills and parameters to execute both position-based and force-based assembly tasks. The authors found only one study presenting a framework of planning and execution robotic systems with deformable objects (Palli et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2019" title="Palli, G., Pirozzi, S., Indovini, M., De Gregorio, D., Zanella, R., & Melchiorri, C. (2019). Automatized switchgear wiring: An outline of the wires experiment results. In Springer tracts in advanced robotics (pp. 107–123). Springer. 
 https://doi.org/10.1007/978-3-030-22327-4_6
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR47" id="ref-link-section-d64739081e827">2019</a>). Palli et al. (<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2019" title="Palli, G., Pirozzi, S., Indovini, M., De Gregorio, D., Zanella, R., & Melchiorri, C. (2019). Automatized switchgear wiring: An outline of the wires experiment results. In Springer tracts in advanced robotics (pp. 107–123). Springer. 
 https://doi.org/10.1007/978-3-030-22327-4_6
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR47" id="ref-link-section-d64739081e830">2019</a>) developed a framework for an industrial system suitable for switchgear manufacturing which includes wiring. The system extracts data from spreadsheets generated during the design process and switchgear CAD models to plan operation sequences. The robotic system applies sensor fusion of tactile and vision sensors, and a special end-effector suitable for deformable linear objects (the wires). Robotic switchgear production is very challenging and an important industrial application. However, the system is not planned from a generic view and the adaptability of the developed framework to additional applications of robotic manufacturing with deformable objects is not clear.</p><div class="c-article-table" data-test="inline-table" data-container-section="table" id="table-1"><figure><figcaption class="c-article-table__figcaption"><b id="Tab1" data-test="table-caption">Table 1 Literature comparison</b></figcaption><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="table-link" data-track="click" data-track-action="view table" data-track-label="button" rel="nofollow" href="/article/10.1007/s10845-025-02578-5/tables/1" aria-label="Full size table 1"><span>Full size table</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div></div></div></section><section data-title="Method"><div class="c-article-section" id="Sec8-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Sec8">Method</h2><div class="c-article-section__content" id="Sec8-content"><p>The developed robotic assembly planning and execution framework has three modules (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s10845-025-02578-5#Fig1">1</a>): data extraction, process planning, and process execution. The modules are interconnected through a central database. The data extraction module retrieves required data from product design and manufacturing documents, determines interactions between parts, and computes required robotic end-point configurations at the part and assembly levels. The process planning module determines sequences of assembly operations (modelled as parametrized hybrid automata with collaborating agents) and calculates the required parameters. Process execution involves real-time production control by a digital twin or in a physical setup. The execution outcome is documented in the database for sequence testing, validation, and production monitoring.</p><p>Parametrized assembly operations, e.g., prepare wire for insertion (for several types of wire), insert pin (for a range of pin diameters) performed by collaboration between production actors e.g., a robotic arm and a gripper, are modelled, validated, and documented in the database a-priori. The model of assembly operations is based on a two-level abstraction of actor operation. At the lower level, parametrized skills model motion profiles, e.g., move to a position, close gripper. The operation of each production actor is modelled as a hybrid automata model of these robotic skills. At the higher level, collaboration between actors is modeled by combing the hybrid automata models in series or in parallel. The models and their combination are validated, e.g., by testing safety and liveliness, during their development. The parametrization and the documentation in the database facilitated operation reuse.</p><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-1" data-title="Fig. 1"><figure><figcaption><b id="Fig1" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 1</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><a class="c-article-section__figure-link" data-test="img-link" data-track="click" data-track-label="image" data-track-action="view figure" href="/article/10.1007/s10845-025-02578-5/figures/1" rel="nofollow"><picture><source type="image/webp" srcset="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs10845-025-02578-5/MediaObjects/10845_2025_2578_Fig1_HTML.png?as=webp"><img aria-describedby="Fig1" src="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs10845-025-02578-5/MediaObjects/10845_2025_2578_Fig1_HTML.png" alt="figure 1" loading="lazy" width="685" height="373"></picture></a></div><div class="c-article-section__figure-description" data-test="bottom-caption" id="figure-1-desc"><p>A framework for planning and execution of robotic assembly processes with deformable objects</p></div></div><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="article-link" data-track="click" data-track-label="button" data-track-action="view figure" href="/article/10.1007/s10845-025-02578-5/figures/1" data-track-dest="link:Figure1 Full size image" aria-label="Full size image figure 1" rel="nofollow"><span>Full size image</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div><h3 class="c-article__sub-heading" id="Sec9">Rαβγ database</h3><p>The Rαβγ categorization was adapted into an ontology to form a relational database suitable for driving robotic assembly planning and execution (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s10845-025-02578-5#Fig2">2</a>). The Rαβγ categorization tiers and categories are translated into tables, as well as the relationships between them.</p><p>The workcell (α) data tables are divided into the actor (production agents) and the material categories. The actor category tables document the actor description with skills the actor possesses, e.g., moving to a target or inserting a peg. In addition, actors required to assemble a product are documented in the workstation table. The material category tables document part and product data such as the part’s type, the product’s industry domain, and their parameters (e.g., size, deformation). Interactions include geometric, physical, and manufacturing attributes (Shneor & Berman, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2022b" title="Shneor, R., & Berman, S. (2022b). Assembly sequence planning with deformable linear objects in the smart factory: Dilemmas and injections. IFAC-PapersOnLine, 55(10), 2457–2462. 
 https://doi.org/10.1016/j.ifacol.2022.10.077
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR56" id="ref-link-section-d64739081e1171">2022b</a>). Interactions during the assembly process are documented in the interaction table. Each interaction is defined by an interaction identifier, connection type (e.g., peg-in-hole), participating parts, and interaction constraints, e.g., priority constraints define priority with respect to other interactions. An example of a priority interaction for a medical infusion kit product is: {‘id’: 6, ‘connection_type’: ‘glue’, ‘materials’: [‘hose_1’, ‘nozzle_1’], ‘priority’: [5]}. This interaction demonstrates a connection type of glue between a hose and a nozzle, where the constraint for this interaction is a priority constraint.</p><p>The task (β) data tables are divided into the manipulation and production categories. Manipulation category tables document low-level robotic skills that encompass parametrization (e.g., locations, forces) for each actor. The production category tables document high-level assembly operations, which encompass production-based knowledge based on hybrid automata state machines. An assembly operation may consist of several robotic skills and material types. The assembly operations are constructed as generic to allow reuse in different applications while maintaining an accurate definition of essential operation features. Accordingly, assembly operations are defined for material types (materials family) and not for a specific material (for example, material type hose with a diameter of 10 mm) and a group of actors. The connection between an assembly operation and a skill is made via the collaborating actors and their parameters. Assembly operations are performed by actors, where the actor’s activity in the assembly operation is defined by a hybrid automata model of skills. The actor models of the robotic skills are combined, forming the assembly operation. The assembly sequences and execution order are documented in the assembly sequences table. The objective (γ) tables document objective measurement data, such as measure type (e.g., speed) for assembly operations.</p> <div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-2" data-title="Fig. 2"><figure><figcaption><b id="Fig2" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 2</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><a class="c-article-section__figure-link" data-test="img-link" data-track="click" data-track-label="image" data-track-action="view figure" href="/article/10.1007/s10845-025-02578-5/figures/2" rel="nofollow"><picture><source type="image/webp" srcset="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs10845-025-02578-5/MediaObjects/10845_2025_2578_Fig2_HTML.png?as=webp"><img aria-describedby="Fig2" src="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs10845-025-02578-5/MediaObjects/10845_2025_2578_Fig2_HTML.png" alt="figure 2" loading="lazy" width="685" height="395"></picture></a></div><div class="c-article-section__figure-description" data-test="bottom-caption" id="figure-2-desc"><p>Rαβγ database, main tables, and connections (partial) between them</p></div></div><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="article-link" data-track="click" data-track-label="button" data-track-action="view figure" href="/article/10.1007/s10845-025-02578-5/figures/2" data-track-dest="link:Figure2 Full size image" aria-label="Full size image figure 2" rel="nofollow"><span>Full size image</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div><h3 class="c-article__sub-heading" id="Sec10">Data extraction</h3><p>Data is automatically extracted from a CAD model and the electric scheme where relevant. Additional data is integrated through a structured user interface that guides the user in retrieving required data from the job guide and datasheets, e.g., port indexes and distance between ports. The extraction methods were fully developed for peg-in-hole interactions. Inserting a peg into a hole is a very common interaction in industrial assembly processes and, in many cases, involves deformable objects (Jiang et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2022" title="Jiang, Y., Huang, Z., Yang, B., & Yang, W. (2022). A review of Robotic Assembly Strategies for the full operation procedure: Planning, execution and evaluation. Robotics and Computer-Integrated Manufacturing, 78, 102366. 
 https://doi.org/10.1016/j.rcim.2022.102366
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR25" id="ref-link-section-d64739081e1205">2022</a>; Panzer & Bender, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2021" title="Panzer, M., & Bender, B. (2021). Deep reinforcement learning in production systems: A systematic literature review. International Journal of Production Research, 60(13), 4316–4341. 
 https://doi.org/10.1080/00207543.2021.1973138
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR49" id="ref-link-section-d64739081e1208">2021</a>).</p><p>Critical landmarks for the peg-in-hole interaction are identified in the data extraction module (see Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s10845-025-02578-5#Fig1">1</a>) in two sub-modules, part level and assembly level. At the part level, cylindrical holes frequently found in industry (Jiang et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2020" title="Jiang, J., Huang, Z., Bi, Z., Ma, X., & Yu, G. (2020). State-of-the-art control strategies for robotic PIH Assembly. Robotics and Computer-Integrated Manufacturing, 65, 101894. 
 https://doi.org/10.1016/j.rcim.2019.101894
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR24" id="ref-link-section-d64739081e1217">2020</a>) are automatically identified. A first hole is defined as a point of reference to other identified characteristics. First hole identification can be based on geometric topologies calculations of closed wires on surfaces, and the intersection of auxiliary lines with the closed wires. For deformable objects, additional information (e.g., holes indexing, type of insertion) is extracted from relevant manufacturing documents (e.g., datasheets, job guides).</p><p>For each hole relevant to a peg-in-hole interaction, three configurations are defined for the robotic wrist: Mate, Free, and Safe. Mate is the wrist configuration at the end of the insertion operation (i.e., peg-in-hole). Free is the wrist configuration just before the onset of the insertion operation. Safe is the wrist configuration near the insertion point, in which no collisions are expected. The robot can use a free motion skill to reach the Safe configuration, where preparatory skills may be used, e.g., exposing a wire. An insertion skill is used between the Free and Safe configurations. The Mate configuration is found based on the CAD model and part datasheet. The relative distances between the Mate, Free, and Safe configurations are parametrized. The configurations are defined in both part and assembly coordinates to facilitate position adaptation with respect to the part. At the assembly level, parts can be positioned in different configurations thus requiring coordinate transformations. Notation and pseudocodes for the part and product calculations of Mate, Free, and Safe configurations based on rotation and transformation matrices are described in Appendix <a data-track="click" data-track-label="link" data-track-action="section anchor" href="/article/10.1007/s10845-025-02578-5#Sec21">A</a>.</p><h3 class="c-article__sub-heading" id="Sec11">Process planning</h3><p>The process planning module (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s10845-025-02578-5#Fig1">1</a>) contains two sub-modules: sequence planning and sequence parameter calculation. Sequence planning is based on the heuristic generation of feasible sequences of assembly operations and the selection of a subset of the highest-performing sequences (Ben-David et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2021" title="Ben-David, S., Shneor, R., Zuler, S., Mann, Z., Greenberg, A., & Berman, S. (2021). Simulation-based two stage sequencing of robotic assembly operations with deformable objects. IFAC-PapersOnLine, 54(1), 175–180. 
 https://doi.org/10.1016/j.ifacol.2021.08.020
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR4" id="ref-link-section-d64739081e1238">2021</a>). The subset of highest performing sequences is later tested and validated by the process execution module with the digital twin. Prior to execution, required parameters are computed for each assembly operation in the sequence. The sequences and the computed parameters are stored in the Rαβγ database.</p><p>The heuristic sequence generation is conducted using a genetic algorithm, where each assembly operation is considered a “gene,” and their sequence constitutes a “chromosome,” which codes a possible solution to the sequencing problem. The algorithm is initiated by generating an initial population of sequences of assembly operations selected from the database according to product and station characteristics, e.g., object material, production equipment, and interaction types (Ben-David & Berman, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2023" title="Ben-David, S., & Berman, S. (2023). A multi-objective fitness function for sequencing robotic assembly operations with deformable objects using a genetic algorithm with constraint satisfaction. In Intelligent and transformative production in pandemic times. Lecture notes in production engineering (pp. 175–185). Springer. 
 https://doi.org/10.1007/978-palli3-031-18641-7_17
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR5" id="ref-link-section-d64739081e1244">2023</a>). Starting from the initial population, at each iteration of the genetic algorithm, new possible sequences are generated using mutation and crossover operators, and the feasibility of a sequence is verified by checking if it includes all the parts in the product and if it adheres to all the assembly constraints. The performance of the feasible sequences is assessed using a fitness function, and the best-performing subset of sequences is retained for the next iteration.</p><p>The characteristics of the initial population largely affect both genetic algorithm convergence speed and, therefore, the quality of the attained solution (Lin & Gen, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2018" title="Lin, L., & Gen, M. (2018). Hybrid evolutionary optimisation with learning for production scheduling: State-of-the-art survey on algorithms and applications. International Journal of Production Research, 56(1–2), 193–223. 
 https://doi.org/10.1080/00207543.2018.1437288
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR35" id="ref-link-section-d64739081e1250">2018</a>). Constraint satisfaction solution methods can assist in expediting the generation of feasible sequences for the initial population (Zouita et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2019" title="Zouita, M., Bouamama, S., & Barkaoui, K. (2019). Improving genetic algorithm using arc consistency technic. Procedia Computer Science, 159, 1387–1396. 
 https://doi.org/10.1016/j.procs.2019.09.309
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR79" id="ref-link-section-d64739081e1253">2019</a>). Arc consistency is a constraint satisfaction solution method suitable for handling binary constraints, e.g., operation priority or proximity constraints, commonly applied in assembly operation sequencing problems. The arc consistency3 (AC3) algorithm balances simplicity with efficiency, maintaining a relatively small number of data structures when performing the solution search (Li, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2017" title="Li, H. (2017). Narrowing support searching range in maintaining arc consistency for solving constraint satisfaction problems. Ieee Access : Practical Innovations, Open Solutions, 5, 5798–5803. 
 https://doi.org/10.1109/access.2017.2690672
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR32" id="ref-link-section-d64739081e1256">2017</a>; Zouita et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2019" title="Zouita, M., Bouamama, S., & Barkaoui, K. (2019). Improving genetic algorithm using arc consistency technic. Procedia Computer Science, 159, 1387–1396. 
 https://doi.org/10.1016/j.procs.2019.09.309
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR79" id="ref-link-section-d64739081e1259">2019</a>). Therefore, in the current work, the initial population is generated using AC3, where a lower bound for the number of required assembly operations in all sequences is determined based on the number of parts in the product (Ben-David & Berman, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2023" title="Ben-David, S., & Berman, S. (2023). A multi-objective fitness function for sequencing robotic assembly operations with deformable objects using a genetic algorithm with constraint satisfaction. In Intelligent and transformative production in pandemic times. Lecture notes in production engineering (pp. 175–185). Springer. 
 https://doi.org/10.1007/978-palli3-031-18641-7_17
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR5" id="ref-link-section-d64739081e1262">2023</a>).</p><p>The population size, which refers to the number of potential assembly sequences evaluated in each iteration of the genetic algorithm, is a significant factor impacting complexity. As population size grows, computational complexity increases, potentially leading to longer runtimes. Population growth can result in significantly higher computation time, demanding substantial computing resources (Pyrih et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2024" title="Pyrih, Y., Klymash, M., Kaidan, M., Hordiichuk-Bublivska, O., & Nodzhak, L. (2024). Investigating the computational complexity of the genetic algorithm with variations in population size and the number of generations. In 2024 IEEE 17th International Conference on Advanced Trends in Radioelectronics Telecommunications and Computer Engineering (TCSET) (Vol. 1, pp. 1–4). 
 https://doi.org/10.1109/tcset64720.2024.10755729
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR50" id="ref-link-section-d64739081e1268">2024</a>). Adding constraint satisfaction requirements (e.g., using AC3) to the generation process of the initial population assists in reducing the number of required iterations. Although this adds computational complexity to the initial population generation stage it is typically worthwhile since the AC3 algorithm exhibits polynomial time complexity (Dechter & Pearl, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 1987" title="Dechter, R., & Pearl, J. (1987). Network-based heuristics for constraint-satisfaction problems. Artificial Intelligence, 34(1), 1–38. 
 https://doi.org/10.1016/0004-3702(87)90002-6
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR14" id="ref-link-section-d64739081e1271">1987</a>).</p><p>Weighing the resemblance to an existing successful sequence can assist in establishing high-quality sequences. A known method for assessing the resemblance between sequences is the longest common subsequence (LCS). LCS is a heuristic that examines the relative order of operations of two sequences, looking for the longest series of common operations without mandating a consecutive appearance of operations (Goyal et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2013" title="Goyal, K. K., Jain, P. K., & Jain, M. (2013). A comprehensive approach to operation sequence similarity based part family formation in the reconfigurable Manufacturing System. International Journal of Production Research, 51(6), 1762–1776. 
 https://doi.org/10.1080/00207543.2012.701771
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR18" id="ref-link-section-d64739081e1278">2013</a>). This means that for the subsequence identified, the same operations are executed with the same precedence relations. LCS reflects precedence relations between operations, which are common production constraints. Therefore, using the score for LCS can improve the production quality of the found sequence. To enhance efficiency and quality, a multi-objective fitness function incorporating LCS and estimated sequence duration was employed (Ben-David & Berman, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2023" title="Ben-David, S., & Berman, S. (2023). A multi-objective fitness function for sequencing robotic assembly operations with deformable objects using a genetic algorithm with constraint satisfaction. In Intelligent and transformative production in pandemic times. Lecture notes in production engineering (pp. 175–185). Springer. 
 https://doi.org/10.1007/978-palli3-031-18641-7_17
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR5" id="ref-link-section-d64739081e1281">2023</a>). The projected duration for each assembly operation was derived from estimations provided by industry experts, considering the industry sector and its associated product type. After the generation of assembly sequences, pertinent parameters are computed for each sequence selected. The parameters include operational parameter values, e.g., the specific gripper opening required, and waypoint configurations required between and within operations, i.e., forming a robotic motion path.</p><h3 class="c-article__sub-heading" id="Sec12">Process execution</h3><p>The process execution module comprises three sub-modules (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s10845-025-02578-5#Fig1">1</a>): Mission control, a digital twin, and a physical controller. Mission control connects the central database and the digital twin or the physical robot controller, and through this connection, it controls and facilitates high-level visualization of the assembly operation sequence performed. The digital twin is based on the physical simulation model of the production process environment. The physical controller controls the operations in the physical environment.</p><p>The Mission control sub-module controls and validates execution at the assembly sequence level. Mission control interacts with the digital twin and the physical controller, the Rαβγ database, and the human operator. Based on the data in the Rαβγ database, the user can view available sequences for producing a product and their past performance. Process metrics include operation duration, collisions, near misses (when the robot is closer than a predefined distance), etc. The user can select a sequence for production and target production environment (physical, virtual, or both). After execution is initiated Mission control sends operations, and their parameters sequentially based on the selected sequence to the target controllers. Each operation is sent after the target controllers return process indicators regarding the previous operation. The process indicators are stored in the Rαβγ database. The sequence execution stage is graphically presented to the user during production. In case of failure, e.g., collision production is halted.</p><p>The environment and production agents and operations are modelled with a digital twin software. The operations programmed in the digital twin are manifestations of the parametrized hybrid automata models. The operations in the sequence planned by the process planning module can be interpreted by the controller and transformed into physical and dynamic processes. This transformation involves transforming endpoint configurations to robot joint configurations, generation of motor control signals based on required motion profiles (e.g., jointed, linear, circular), and coordination of inputs and outputs (e.g., transducers and optical sensors) to ensure precise and synchronized assembly. The digital twin software includes an integrated virtual controller that mirrors the functionality of the physical counterparts in the work cell. When the digital twin receives an operation and parameters from the Mission control, it executes the programmed operation using the virtual controller. Upon successful execution of the program by the digital twin, and following validation of process metrics (e.g., operation duration, collisions), Mission control proceeds to transmit identical configuration settings, operations, and parameters to the physical controller. The physical control manages the physical hardware components of the robotic system, encompassing input and output components, actuators, and the robotic platform itself. Sensors within the physical setup measure and give feedback to the physical controller regarding the process advancement and changes in the execution (e.g., object deformation). Using this setup, learned hybrid automata, e.g., impedance-controlled deformable peg insertion demonstrated zero-shot policy transfer to the physical controller (Kozlovsky et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2022" title="Kozlovsky, S., Newman, E., & Zacksenhouse, M. (2022). Reinforcement learning of impedance policies for peg-in-hole tasks: Role of asymmetric matrices. IEEE Robotics and Automation Letters, 7(4), 10898–10905. 
 https://doi.org/10.1109/lra.2022.3191070
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR30" id="ref-link-section-d64739081e1301">2022</a>).</p></div></div></section><section data-title="Case study"><div class="c-article-section" id="Sec13-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Sec13">Case study</h2><div class="c-article-section__content" id="Sec13-content"><p>The Rαβγ assembly planning and execution framework was tested using a case study approach. The case studies chosen are of actual industrial products or very close prototypes. Such a case-study approach facilitates an examination of the suitability of the concepts upon which the method was developed for the targeted assembly processes with deformable objects. The main difficulty in the developed framework is the integration of all the different components. The case study approach is a common method for examining integration capabilities (Goujon et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2024" title="Goujon, A., Rosin, F., Magnani, F., Lamouri, S., Pellerin, R., & Joblot, L. (2024). Industry 5.0 use cases development framework. International Journal of Production Research. 
 https://doi.org/10.1080/00207543.2024.2307505
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR17" id="ref-link-section-d64739081e1313">2024</a>). The case studies selected contained rigid and linear deformable objects from two industry sectors: electrical and medical. These industries were selected as representative industries for products with and without electronic components. The final demonstration was conducted at the shop floor level of a small local factory and operated by production personnel.</p><h3 class="c-article__sub-heading" id="Sec14">Environment</h3><p>The Rαβγ database was built using MySQL Workbench version 8.0.21 with a cloud-based Amazon Web Services (AWS) environment. Input contained the following manufacturing documents: each part’s CAD model and datasheet, an electric scheme for electrical parts, CAD models of the assemblies, and job guides. The user interface and geometric computations were performed in MATLAB R2022a, 64-bit version. Automatic data extraction from CAD models and process planning was implemented using PythonOCC (Tpaviot, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2008" title="Tpaviot, T. (2008). Tpaviot/PYTHONOCC-core: Python package for 3D CAD/BIM/PLM/Cam. GitHub. 
 https://github.com/tpaviot/pythonocc-core
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR62" id="ref-link-section-d64739081e1323">2008</a>) and Python 3.8 with PyCharm (JetBrains, The Netherlands) environment. Mission control and the digital twin were programmed with Tecnomatix Process Simulate<sup><a href="#Fn1"><span class="u-visually-hidden">Footnote </span>1</a></sup> version 16.1.2 (Siemens, Germany). The deformable linear objects were modelled using the Kineo Flexible Cables module (Siemens, Germany), a physics-based solver for configurations and deformations in response to machine motions. Motion planning is performed using the digital twin toolset, facilitating software commissioning directly to the robot’s controller. The physical robotic work cell was implemented on a single UR5e with Intel@RealSense LIDAR Camera L515 for reorientation and path replanning. A designated end-effector was developed with force sensors on the fingers to manipulate deformable linear objects. The end-effector has an integrated mechanism to strip and cut the electric wire. The operating system for the robot and motion controller was Ubuntu 20.04.5 LTS, running on an Intel Core i7-8559U 2.70 GHz processor with 8GB RAM. The operating system for the vision system was Windows 10 64-bit, running on an Intel Core i9-10900X, up to 4.50 GHz processor with 64GB RAM.</p><h3 class="c-article__sub-heading" id="Sec15">Products</h3><p>The assemblies contained commercial components with rigid parts and deformable linear objects. Three case studies were presented and are described in Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s10845-025-02578-5#Fig3">3</a> (photos and CAD models). All three case studies contained peg-in-hole connections. Two from the electrical industry and one from the medical sector. The products were developed by commercial manufacturers. The electrical case studies are based on an actual product manufactured by a lighting company<sup><a href="#Fn2"><span class="u-visually-hidden">Footnote </span>2</a></sup> that was adopted to exemplify the methodology. Therefore, the assemblies closely model real-world products. The two electrical assemblies are smart light boards with 3D rigid parts and a wire with plastic deformation. The medical assembly is a medical infusion kit developed by a commercial manufacturer<sup><a href="#Fn3"><span class="u-visually-hidden">Footnote </span>3</a></sup> with a deformable tube with elastic deformation and two connectors. A reduced light board (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s10845-025-02578-5#Fig3">3</a>a) contained a terminal block, a 60 W driver, and three 3 mm wires connecting the terminal and driver ports. An extended light board (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s10845-025-02578-5#Fig3">3</a>b) contained a terminal block, a 60 W driver in a different orientation from the reduced light board, a 75 W driver, and three 3 mm wires connecting the terminal ports and the 75 W driver ports and two 3 mm wires connecting between the 75 W driver ports and the 60 W driver ports. A medical infusion kit (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s10845-025-02578-5#Fig3">3</a>c) contained an 8 mm tube with elastic deformation and two funnel connectors with elastic deformation. The tube is dipped into a solvent container and inserted into the funnels.</p><p>The reduced and extended light boards include wires which are relatively light deformable objects characterized by plastic deformations. For this case the strategy of reducing process uncertainty was applied (Shneor & Berman, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2023b" title="Shneor, R., & Berman, S. (2023b). Robotic Assembly with deformable objects. In Systems collaboration and integration (pp. 221–235). Springer. 
 https://doi.org/10.1007/978-3-031-44373-2_13
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR58" id="ref-link-section-d64739081e1386">2023b</a>). Process uncertainty was reduced using an advanced end-effector with an integrated wire cutting and exposing mechanism through which wire flows directly from a connected wire drum. The deformable object in the medical infusion kit, the flow tube is heavier than the wires and it is characterized by elastic deformations. In this case, the strategy of reducing the susceptibility of the manufacturing system to uncertainty was applied. This was achieved by taking the elastic deformations into account during dynamic motion planning. The Kineo Flexible Cables commercial package embedded in the digital twin was used to model the tube and integrate the deformations with dynamic motion planning.</p> <div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-3" data-title="Fig. 3"><figure><figcaption><b id="Fig3" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 3</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><a class="c-article-section__figure-link" data-test="img-link" data-track="click" data-track-label="image" data-track-action="view figure" href="/article/10.1007/s10845-025-02578-5/figures/3" rel="nofollow"><picture><source type="image/webp" srcset="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs10845-025-02578-5/MediaObjects/10845_2025_2578_Fig3_HTML.png?as=webp"><img aria-describedby="Fig3" src="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs10845-025-02578-5/MediaObjects/10845_2025_2578_Fig3_HTML.png" alt="figure 3" loading="lazy" width="685" height="633"></picture></a></div><div class="c-article-section__figure-description" data-test="bottom-caption" id="figure-3-desc"><p>Case studies’ photos (left) and 3D models (right): reduced light board (<b>a</b>), extended light board (<b>b</b>), and medical infusion kit (<b>c</b>)</p></div></div><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="article-link" data-track="click" data-track-label="button" data-track-action="view figure" href="/article/10.1007/s10845-025-02578-5/figures/3" data-track-dest="link:Figure3 Full size image" aria-label="Full size image figure 3" rel="nofollow"><span>Full size image</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div><h3 class="c-article__sub-heading" id="Sec16">Data extraction</h3><p>Data from CAD models and the electric schemes were extracted automatically using the PythonOCC library. The holes in the CAD models were automatically identified by calculating close wires on surfaces that intersected an auxiliary line. The robotic wrist configurations (position and orientation) were derived based on the CAD files, where orientations were represented using quaternions. The Mate, Free, and Safe configuration of holes in the assembly coordinate systems were calculated based on the part configuration in the CAD assembly model.</p><p>A user interface was developed for data based on datasheets and the job guide for parts with indexing and parameters such as distance between cylinders or cylinders indexing (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s10845-025-02578-5#Fig4">4</a>a). Parameters at the part level (e.g., the total number of ports) and the assembled product level (e.g., wrist safe distance) were adapted to data easily deductible from part datasheets, e.g., the distance between ports and first and last port index. For the assembly level, the parts include relevant robotic cell parameters such as wrist safety distances (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s10845-025-02578-5#Fig4">4</a>b). The configuration data automatically extracted can be recalled from the database and presented to the user, with editing capabilities.</p> <div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-4" data-title="Fig. 4"><figure><figcaption><b id="Fig4" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 4</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><a class="c-article-section__figure-link" data-test="img-link" data-track="click" data-track-label="image" data-track-action="view figure" href="/article/10.1007/s10845-025-02578-5/figures/4" rel="nofollow"><picture><source type="image/webp" srcset="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs10845-025-02578-5/MediaObjects/10845_2025_2578_Fig4_HTML.png?as=webp"><img aria-describedby="Fig4" src="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs10845-025-02578-5/MediaObjects/10845_2025_2578_Fig4_HTML.png" alt="figure 4" loading="lazy" width="685" height="639"></picture></a></div><div class="c-article-section__figure-description" data-test="bottom-caption" id="figure-4-desc"><p>User interface screens, part level (<b>a</b>) and assembly level (<b>b</b>)</p></div></div><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="article-link" data-track="click" data-track-label="button" data-track-action="view figure" href="/article/10.1007/s10845-025-02578-5/figures/4" data-track-dest="link:Figure4 Full size image" aria-label="Full size image figure 4" rel="nofollow"><span>Full size image</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div><h3 class="c-article__sub-heading" id="Sec17">Process planning</h3><p>The actors modeled include: a robotic arm, a wire feeder, and a gripper. Hybrid automata models were developed for each actor and composed to describe assembly operations relevant for all three use cases. Overall, 61 assembly operations were modeled. As an example, the “prepare wire” operation is depicted in Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s10845-025-02578-5#Fig5">5</a>.</p> <div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-5" data-title="Fig. 5"><figure><figcaption><b id="Fig5" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 5</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><a class="c-article-section__figure-link" data-test="img-link" data-track="click" data-track-label="image" data-track-action="view figure" href="/article/10.1007/s10845-025-02578-5/figures/5" rel="nofollow"><picture><source type="image/webp" srcset="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs10845-025-02578-5/MediaObjects/10845_2025_2578_Fig5_HTML.png?as=webp"><img aria-describedby="Fig5" src="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs10845-025-02578-5/MediaObjects/10845_2025_2578_Fig5_HTML.png" alt="figure 5" loading="lazy" width="685" height="383"></picture></a></div><div class="c-article-section__figure-description" data-test="bottom-caption" id="figure-5-desc"><p>Exposing wire tip operation: an example of a hybrid automata model with three actors: robotic arm, gripper, and wire feeder (presentation based on Lee and Seshia (<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2017" title="Lee, E. A., & Seshia, S. A. (2017). Introduction to embedded systems—a cyber-physical systems approach (2nd ed.). MIT." href="/article/10.1007/s10845-025-02578-5#ref-CR31" id="ref-link-section-d64739081e1488">2017</a>)</p></div></div><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="article-link" data-track="click" data-track-label="button" data-track-action="view figure" href="/article/10.1007/s10845-025-02578-5/figures/5" data-track-dest="link:Figure5 Full size image" aria-label="Full size image figure 5" rel="nofollow"><span>Full size image</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div><p>The assembly operations recorded in the database form the genes for establishing chromosomes for the genetic algorithm. For each use case, a subset of the operations was selected, based on the actors in the work cell, the product domain (electric or medical), and the interactions (connection) defined for each part in the product (place, insert, glue, etc.). An initial population of 10 chromosomes was created for each use case, where chromosome length was defined by the number of interactions in the product, and priority constraints were defined based on connection height for each part (lower connections must be connected before higher connections) (Ben-David & Berman, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2023" title="Ben-David, S., & Berman, S. (2023). A multi-objective fitness function for sequencing robotic assembly operations with deformable objects using a genetic algorithm with constraint satisfaction. In Intelligent and transformative production in pandemic times. Lecture notes in production engineering (pp. 175–185). Springer. 
 https://doi.org/10.1007/978-palli3-031-18641-7_17
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR5" id="ref-link-section-d64739081e1502">2023</a>). The fitness function was defined as an equally weighted sum of process duration and LCS. Process duration was defined as the sum of the duration of each assembly operation in the sequence. LCS was computed with respect to sequences previously found and validated using the digital twin for reminiscent product variants, i.e., two other smart light panels and medical infusion kits (Ben-David and Berman, 2021). The mutation rate was 0.05 and the crossover rate was 0.5 to balance creation of new solutions with maintenance of high-quality solutions from previous generations. The stopping criteria were set as a series of three consecutive generations with a change in the fitness function below 0.01 (Ben-David et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2021" title="Ben-David, S., Shneor, R., Zuler, S., Mann, Z., Greenberg, A., & Berman, S. (2021). Simulation-based two stage sequencing of robotic assembly operations with deformable objects. IFAC-PapersOnLine, 54(1), 175–180. 
 https://doi.org/10.1016/j.ifacol.2021.08.020
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR4" id="ref-link-section-d64739081e1505">2021</a>; Ben-David & Berman, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2023" title="Ben-David, S., & Berman, S. (2023). A multi-objective fitness function for sequencing robotic assembly operations with deformable objects using a genetic algorithm with constraint satisfaction. In Intelligent and transformative production in pandemic times. Lecture notes in production engineering (pp. 175–185). Springer. 
 https://doi.org/10.1007/978-palli3-031-18641-7_17
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR5" id="ref-link-section-d64739081e1508">2023</a>). The sequences found using the genetic algorithm heuristic were validated using the digital twin.</p><h3 class="c-article__sub-heading" id="Sec18">Process execution</h3><p>Mission control, a process execution module was developed for Process Simulate software. Mission control connects the Rαβγ database, and the digital twin or the physical controller. Using a graphic interface, Mission control facilitated visualizing the assembly sequence operations and relevant actors based on the documentation in the Rαβγ database, e.g., the medical tube sequence operations and UR5e robotic arm (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s10845-025-02578-5#Fig6">6</a>a).</p><p>Mission control also enabled virtual tests and metrics definitions such as total time, allowing collisions (yes/no) and near-miss distance. Mission control allowed running assembly sequences, and the metrics results were highlighted in green if the results met the metrics definitions or in red if the results did not meet the metrics definitions (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s10845-025-02578-5#Fig6">6</a>b). An interface based on JavaScript Object Notation was developed for interaction with the process execution module.</p><p>The three case studies were executed on the digital twin and the physical controller (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s10845-025-02578-5#Fig3">3</a> and the supplementary materials Online Resource 1 video depicting the medical pipeline digital twin execution, Online Resource 2 video depicting the medical pipeline physical robot execution, and Online Resource 3 video depicting the extended light board physical robot execution). For the extended light board, 86% of the assembly operations were reused and for the reduced light board, 78% of the assembly operations were reused (Table <a data-track="click" data-track-label="link" data-track-action="table anchor" href="/article/10.1007/s10845-025-02578-5#Tab2">2</a>). The reused assembly operations include picking, exposing, and snapping the wire into the relevant part. The high rate of reused operations in the extended light board is due to the multiplicity of the snap operation in the production sequence. For the medical infusion kit, 62% of the assembly operations were reused. These reused operations include picking, dipping the tube in and out of the solvent, inserting the tube into the funnel, and force-controlled releasing the tube.</p> <div class="c-article-table" data-test="inline-table" data-container-section="table" id="table-2"><figure><figcaption class="c-article-table__figcaption"><b id="Tab2" data-test="table-caption">Table 2 Assembly operations reusability</b></figcaption><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="table-link" data-track="click" data-track-action="view table" data-track-label="button" rel="nofollow" href="/article/10.1007/s10845-025-02578-5/tables/2" aria-label="Full size table 2"><span>Full size table</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div> <div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-6" data-title="Fig. 6"><figure><figcaption><b id="Fig6" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 6</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><a class="c-article-section__figure-link" data-test="img-link" data-track="click" data-track-label="image" data-track-action="view figure" href="/article/10.1007/s10845-025-02578-5/figures/6" rel="nofollow"><picture><source type="image/webp" srcset="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs10845-025-02578-5/MediaObjects/10845_2025_2578_Fig6_HTML.png?as=webp"><img aria-describedby="Fig6" src="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs10845-025-02578-5/MediaObjects/10845_2025_2578_Fig6_HTML.png" alt="figure 6" loading="lazy" width="685" height="420"></picture></a></div><div class="c-article-section__figure-description" data-test="bottom-caption" id="figure-6-desc"><p>Mission control screenshots of assembly sequencing (<b>a</b>) and metrics definitions (<b>b</b>). Green and red highlight measures that meet or did not meet specifications, respectively (Color figure online)</p></div></div><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="article-link" data-track="click" data-track-label="button" data-track-action="view figure" href="/article/10.1007/s10845-025-02578-5/figures/6" data-track-dest="link:Figure6 Full size image" aria-label="Full size image figure 6" rel="nofollow"><span>Full size image</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div></div></div></section><section data-title="Discussion"><div class="c-article-section" id="Sec19-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Sec19">Discussion</h2><div class="c-article-section__content" id="Sec19-content"><p>Adapting the Rαβγ categorization to a database ontology contributed to the structured documentation of the assembly operations. The structure of the Rαβγ database, which includes the resources (i.e., the workcell, α), the process actions (i.e., the task, β), and the measurements (i.e., the objective, γ), facilitates representing the three planning and execution modules (data extraction, process planning, and process execution) and their interactions. The integration of these components in the database contributed to a substantial reuse of assembly operations, thus potentially reducing setup times.</p><p>The data-driven approach simplifies process planning with multiple entities and complex data structures, facilitates process abstraction, and enables skill and process reuse. However, both planning and execution rely on data availability and quality. Taking a different development approach, agent-based modelling concentrates on the participating agents, their capabilities, and their interactions. Agent-based modelling applies rules, constraints, and assumptions regarding how agents interact with other agents and the environment. Agent-based modelling facilitates addressing complex agent behaviour and is suited for scenarios where interactions are central to the process (Li & Vanhaverbeke, 2021). The agent-based approach facilitates handling multiple large-scale interactions and multitasking. Holons are autonomous and cooperative units within a larger system, consisting of information processing and often physical processing components (Van Brussel et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 1998" title="Van Brussel, H., Wyns, J., Valckenaers, P., Bongaerts, L., & Peeters, P. (1998). Reference architecture for holonic manufacturing systems: Prosa. Computers in Industry, 37(3), 255–274. 
 https://doi.org/10.1016/s0166-3615(98)00102-x
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR66" id="ref-link-section-d64739081e1702">1998</a>). Based on holons and advancements in a virtual representation of physical parts and systems (i.e., digital twins), the ARTI (Activity-Resource-Type-Instance) was developed and implemented (Valckenaers, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2020" title="Valckenaers, P. (2020). Perspective on holonic manufacturing systems: Prosa becomes arti. Computers in Industry, 120, 103226. 
 https://doi.org/10.1016/j.compind.2020.103226
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR65" id="ref-link-section-d64739081e1705">2020</a>; Wasserman et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2023" title="Wasserman, A., Kruger, K., & Basson, A. H. (2023). ARTI-Based Holonic Manufacturing execution system using the BASE Architecture: A case study implementation. In von K. Leipzig, N. Sacks, & M. Mc Clelland (Eds.), Smart, sustainable manufacturing in an ever-changing world. Lecture notes in production engineering. Springer. 
 https://doi.org/10.1007/978-3-031-15602-1_4
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR71" id="ref-link-section-d64739081e1708">2023</a>). Future integration of the presented data-driven approach for work cell operations planning and control with the ARTI architecture may facilitate the integration of lower-level reusability and simplified process planning with higher-level multi-tasking and integration capabilities.</p><p>Planning and control of robotic assembly with deformable objects requires additional information in manufacturing documents such as datasheets, job guides, and electric schemes (for relevant products). These documents contained manufacturing information crucial for planning and execution (e.g., points of interest and safe distances of the robotic actuator). Yet, there is little standardization in the abovementioned documents, and this is an important future work that will further enhance the automation of planning and execution for intricate assemblies. Applying artificial intelligence methods such as text mining as part of data extraction is also left for future research.</p><p>The process planning module highlighted that hybrid automata are applicable for establishing robotic skills using deep neural networks. Deep neural network methods such as reinforcement learning have been applied for production planning and scheduling, mainly with a single agent (Esteso et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2022" title="Esteso, A., Peidro, D., Mula, J., & Díaz-Madroñero, M. (2022). Reinforcement learning applied to production planning and control. International Journal of Production Research, 61(16), 5772–5789. 
 https://doi.org/10.1080/00207543.2022.2104180
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR15" id="ref-link-section-d64739081e1717">2022</a>). Reinforcement learning methods have been demonstrated for specific assembly operations of insertion (Kozlovsky et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2022" title="Kozlovsky, S., Newman, E., & Zacksenhouse, M. (2022). Reinforcement learning of impedance policies for peg-in-hole tasks: Role of asymmetric matrices. IEEE Robotics and Automation Letters, 7(4), 10898–10905. 
 https://doi.org/10.1109/lra.2022.3191070
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR30" id="ref-link-section-d64739081e1720">2022</a>; Liu et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2022" title="Liu, Z., Liu, Q., Xu, W., Wang, L., & Zhou, Z. (2022). Robot learning towards smart robotic manufacturing: A review. Robotics and Computer-Integrated Manufacturing, 77, 102360. 
 https://doi.org/10.1016/j.rcim.2022.102360
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR37" id="ref-link-section-d64739081e1723">2022</a>). However, the learning period is time-consuming, especially when deformable objects are involved. Using parametrized hybrid automata stored in the Rαβγ database, enabled skill reuse. Forming high-level assembly operations (e.g., dipping a deformable tube with solvent) by connecting the low-level skills enabled operation reuse. Reusing operations within the production of a single product (i.e., executing the assembly operation several times with different parameters) and in other products (i.e., according to similar sequences of the genetic algorithms) facilitates shortening setups.</p><p>The process execution module highlighted that the digital twin facilitated an accurate representation of the physical system and virtual validation before the physical execution. Mission control and the digital twin were applied for metric evaluation and validation of robotic skills for assembling products containing deformable objects. They emphasized the importance of integrating the developed framework, e.g., process planning sequencing and parametrization, database storage of assembly operations, and robotic skills. Such integration enabled addressing robotic assembly planning and execution with deformable objects. The Mission control interface contains important metrics (e.g., collisions). However, there might be differences in the emphasis by industry sectors. For example, some electrical manufacturers might emphasize time measures, whereas other medical manufacturers might emphasize quality measures. Future research should investigate and develop designated metrics per industry sector.</p><p>Production sequences were successfully planned and executed for case studies from different industry sectors (electric and medical). The rate of reused operations was high, but the products differed. The medical infusion kit required the assembly of parts with elastic deformations, while for the light boards, rigid parts (connectors, drivers) were assembled with parts with plastic deformations (electric wires). The rheological nature of the assembled parts influenced the rate of reused operations, and the medical infusion kit required dedicated operations, leading to a lower rate of reused operations. The rate of reused operations should be further investigated for additional product types, e.g., packaging.</p><p>Incorporating advanced artificial intelligence models in manufacturing, robotics, and digital twins is a promising direction (Varriale et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2023" title="Varriale, V., Cammarano, A., Michelino, F., & Caputo, M. (2023). Critical analysis of the impact of artificial intelligence integration with cutting-edge technologies for production systems. Journal of Intelligent Manufacturing. 
 https://doi.org/10.1007/s10845-023-02244-8
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR68" id="ref-link-section-d64739081e1736">2023</a>). Recent studies applied deep learning to identify geometric features of electrical components from CAD (Bründl et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2023" title="Bründl, P., Scheffler, B., Stoidner, M., Nguyen, H., Baechler, A., Abrass, A., & Franke, J. (2023). Semantic part segmentation of spatial features via geometric deep learning for Automated Control Cabinet Assembly. Journal of Intelligent Manufacturing, 35(8), 3681–3695. 
 https://doi.org/10.1007/s10845-023-02267-1
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR9" id="ref-link-section-d64739081e1739">2023</a>) and explainable artificial intelligence in manufacturing stages (Naqvi et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2024" title="Naqvi, M. R., Elmhadhbi, L., Sarkar, A., Archimede, B., & Karray, M. H. (2024). Survey on ontology-based explainable AI in manufacturing. Journal of Intelligent Manufacturing, 35(8), 3605–3627. 
 https://doi.org/10.1007/s10845-023-02304-z
 
 " href="/article/10.1007/s10845-025-02578-5#ref-CR42" id="ref-link-section-d64739081e1742">2024</a>). Incorporating advanced artificial intelligence models can facilitate significant potential in improving the data extraction and process planning modules. In the data extraction module, artificial intelligence models and text mining can be applied to automate the extraction of information (e.g., points of interest) from unstructured manufacturing documents. In the process planning module, artificial intelligence models can contribute meaningfully to the assembly sequence generation and maybe replace the genetic algorithm. Future research directions include conducting an ablation study to evaluate the individual contributions of machine learning components in process planning and to validate the incorporation of artificial intelligence models in the data extraction and process planning modules.</p><p>Some limitations were identified in the current work. The developed framework concentrated on known environments where the workstation layout and the locations of the components are known. Known environments are commonly found in industrial environments containing robotics, however, unknown environments can also be found and are more challenging for production planning and execution (e.g., where the location of the parts is not precise). The investigation and adaptation of the presented framework to unknown production environments are left for future research.</p><p>The work concentrated on the peg-in-hole connection type, which is common for assembling parts in industrial products. There are many additional connection types, e.g., thread-in-channel, where a deformable linear object is placed in a narrow, not fully closed space. Such a connection type is common for sealing, e.g., a gasket in a toolbox cover. Another connection type is peg-in-wrap, where a linear object (usually deformable) is used for wrapping another object by coiling or binding, e.g., binding wires in an electrical cabinet. The connection types will be explored in future work.</p><p>The current study implemented case study assemblies with a limited number of operations (less than thirty) to demonstrate the framework and its modules. Future research should investigate more intricate assemblies, e.g., with a higher number of assembly operations, and additional industry sectors, e.g., plastic. Planning and execution of robotic assembly of an industrial computer with hundreds of components is underway.</p></div></div></section><section data-title="Conclusions"><div class="c-article-section" id="Sec20-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Sec20">Conclusions</h2><div class="c-article-section__content" id="Sec20-content"><p>The robotic assembly planning and execution framework comprises a data extraction module, a process planning module, and a process execution module. The three modules are parametrized and stored in a relational database built according to the Rαβγ ontology for robotic manufacturing processes. The framework facilitated the production planning and execution of intricate assemblies containing deformable objects from different industry sectors. The integration of data from commonly applied manufacturing documents is important for future industrial implementation. Using sequences with high-level assembly operations comprised of parametrized robotic skills advances the reuse of assembly operations for varied case studies, which is highly relevant to real-world industrial applications.</p><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-7" data-title="Fig. 7"><figure><figcaption><b id="Fig7" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 7</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><a class="c-article-section__figure-link" data-test="img-link" data-track="click" data-track-label="image" data-track-action="view figure" href="/article/10.1007/s10845-025-02578-5/figures/7" rel="nofollow"><picture><img aria-describedby="Fig7" src="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs10845-025-02578-5/MediaObjects/10845_2025_2578_Fig7a_HTML.png" alt="figure 7" loading="lazy" width="685" height="1134"></picture><picture><img aria-describedby="Fig7" src="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs10845-025-02578-5/MediaObjects/10845_2025_2578_Fig7b_HTML.png" alt="figure 7" loading="lazy" width="685" height="428"></picture></a></div><div class="c-article-section__figure-description" data-test="bottom-caption" id="figure-7-desc"><p>Pseudocodes for part (top) and product, assembly (bottom) calculations</p></div></div><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="article-link" data-track="click" data-track-label="button" data-track-action="view figure" href="/article/10.1007/s10845-025-02578-5/figures/7" data-track-dest="link:Figure7 Full size image" aria-label="Full size image figure 7" rel="nofollow"><span>Full size image</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div></div></div></section><section data-title="Appendix A: Part and assembly levels calculations"><div class="c-article-section" id="Sec21-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Sec21">Appendix A: Part and assembly levels calculations</h2><div class="c-article-section__content" id="Sec21-content"><p>The notation of symbols for calculating Mate, Free, and Safe configurations are listed in Table <a data-track="click" data-track-label="link" data-track-action="table anchor" href="/article/10.1007/s10845-025-02578-5#Tab3">3</a>. The pseudocodes for the part and product (assembly) calculations of Mate, Free, and Safe configurations based on rotation and transformation matrices is described in Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s10845-025-02578-5#Fig7">7</a>.</p> <div class="c-article-table" data-test="inline-table" data-container-section="table" id="table-3"><figure><figcaption class="c-article-table__figcaption"><b id="Tab3" data-test="table-caption">Table 3 Notation</b></figcaption><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="table-link" data-track="click" data-track-action="view table" data-track-label="button" rel="nofollow" href="/article/10.1007/s10845-025-02578-5/tables/3" aria-label="Full size table 3"><span>Full size table</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div></div></div></section> </div> <section data-title="Data availability"><div class="c-article-section" id="data-availability-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="data-availability">Data availability</h2><div class="c-article-section__content" id="data-availability-content"> <p>not applicable.</p> </div></div></section><section data-title="Notes"><div class="c-article-section" id="notes-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="notes">Notes</h2><div class="c-article-section__content" id="notes-content"><ol class="c-article-footnote c-article-footnote--listed"><li class="c-article-footnote--listed__item" id="Fn1" data-counter="1."><div class="c-article-footnote--listed__content"><p><a href="https://plm.sw.siemens.com/en-US/tecnomatix/products/process-simulate-software/">https://plm.sw.siemens.com/en-US/tecnomatix/products/process-simulate-software/</a> Retrieved September 18, 2024.</p></div></li><li class="c-article-footnote--listed__item" id="Fn2" data-counter="2."><div class="c-article-footnote--listed__content"><p><a href="https://www.gaashlighting.com/">https://www.gaashlighting.com/</a> Retrieved September 18, 2024.</p></div></li><li class="c-article-footnote--listed__item" id="Fn3" data-counter="3."><div class="c-article-footnote--listed__content"><p><a href="http://www.mdcindustries.com/home.asp">http://www.mdcindustries.com/home.asp</a> Retrieved September 18, 2024.</p></div></li></ol></div></div></section><div id="MagazineFulltextArticleBodySuffix"><section aria-labelledby="Bib1" data-title="References"><div class="c-article-section" id="Bib1-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Bib1">References</h2><div class="c-article-section__content" id="Bib1-content"><div data-container-section="references"><ul class="c-article-references" data-track-component="outbound reference" data-track-context="references section"><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR1">Abdullah, M. 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The work was supported by Ben-Gurion University of the Negev through the Agricultural, Biological, and Cognitive Robotics Initiative (funded by the Marcus Endowment Fund and the Helmsley Charitable Trust) and by the Israel Innovation Authority as part of the ART (Assembly by Robotic Technology) consortium (grant number 67436).</p></div></div></section><section aria-labelledby="author-information" data-title="Author information"><div class="c-article-section" id="author-information-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="author-information">Author information</h2><div class="c-article-section__content" id="author-information-content"><h3 class="c-article__sub-heading" id="affiliations">Authors and Affiliations</h3><ol class="c-article-author-affiliation__list"><li id="Aff1"><p class="c-article-author-affiliation__address">Department of Industrial Engineering & Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel</p><p class="c-article-author-affiliation__authors-list">Ran Shneor, Shir Ben-David, Bar Shvarzman & Sigal Berman</p></li><li id="Aff2"><p class="c-article-author-affiliation__address">Department of Industrial Engineering & Management, Shamoon College of Engineering, Beer-Sheva, Israel</p><p class="c-article-author-affiliation__authors-list">Gali Naveh</p></li><li id="Aff3"><p class="c-article-author-affiliation__address">Siemens Digital Industries Software, Airport City, Israel</p><p class="c-article-author-affiliation__authors-list">Zachi Mann & Alex Greenberg</p></li><li id="Aff4"><p class="c-article-author-affiliation__address">Polygon-Technologies, Mitzpe Sapir, Zur Yigal, Israel</p><p class="c-article-author-affiliation__authors-list">Yotam Efrat & Omer Einav</p></li></ol><div class="u-js-hide u-hide-print" data-test="author-info"><span class="c-article__sub-heading">Authors</span><ol class="c-article-authors-search u-list-reset"><li id="auth-Ran-Shneor-Aff1"><span class="c-article-authors-search__title u-h3 js-search-name">Ran Shneor</span><div class="c-article-authors-search__list"><div class="c-article-authors-search__item c-article-authors-search__list-item--left"><a href="/search?sortBy=newestFirst&dc.creator=Ran%20Shneor" class="c-article-button" data-track="click" data-track-action="author link - 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SBD, GN, BS, and SB developed the database. RS and SB developed the data extraction module. SBD, BS, and SB developed the process planning module. ZM, AG, YE, and OE developed the process execution module. RS and SB wrote the initial draft of the manuscript. All authors discussed the results and contributed to the final manuscript. All authors read and approved the final manuscript.</p><h3 class="c-article__sub-heading" id="corresponding-author">Corresponding author</h3><p id="corresponding-author-list">Correspondence to <a id="corresp-c1" href="mailto:shneorr@post.bgu.ac.il">Ran Shneor</a>.</p></div></div></section><section data-title="Ethics declarations"><div class="c-article-section" id="ethics-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="ethics">Ethics declarations</h2><div class="c-article-section__content" id="ethics-content"> <h3 class="c-article__sub-heading" id="FPar2">Competing interests</h3> <p>The authors report there are no competing interests to declare.</p> <h3 class="c-article__sub-heading" id="FPar3">Ethical approval</h3> <p>Not applicable.</p> <h3 class="c-article__sub-heading" id="FPar1">Consent for publication</h3> <p>Not applicable.</p> <h3 class="c-article__sub-heading" id="FPar4">Consent to participate</h3> <p>Not applicable.</p> </div></div></section><section data-title="Additional information"><div class="c-article-section" id="additional-information-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="additional-information">Additional information</h2><div class="c-article-section__content" id="additional-information-content"><h3 class="c-article__sub-heading">Publisher’s note</h3><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></div></div></section><section data-title="Supplementary Information"><div class="c-article-section" id="Sec30-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Sec30">Supplementary Information</h2><div class="c-article-section__content" id="Sec30-content"><div data-test="supplementary-info"><div id="figshareContainer" class="c-article-figshare-container" data-test="figshare-container"></div><p>Below is the link to the electronic supplementary material. </p><div id="MOESM1"><div class="video" id="mijsvdivEvya3JvZ6WQSPtXDD4CtVM"><div mi24-video-player="true" video-id="Evya3JvZ6WQSPtXDD4CtVM" player-id="8PcXmCm9nWqE6posBEkd1h" config-type="vmpro" flash-path="//e.video-cdn.net/v2/" api-url="//d.video-cdn.net/play"></div><script src="//e.video-cdn.net/v2/embed.js"></script></div><div class="serif u-mb-0 u-mt-32 standard-space-below" data-test="bottom-caption" id="video-description-Evya3JvZ6WQSPtXDD4CtVM"><p>Supplementary file 2 (mp4 7870 KB)</p></div></div><div id="MOESM2"><div class="video" id="mijsvdiv4Hw9ygQ9pooEpcH71cZEii"><div mi24-video-player="true" video-id="4Hw9ygQ9pooEpcH71cZEii" player-id="8PcXmCm9nWqE6posBEkd1h" config-type="vmpro" flash-path="//e.video-cdn.net/v2/" api-url="//d.video-cdn.net/play"></div><script src="//e.video-cdn.net/v2/embed.js"></script></div><div class="serif u-mb-0 u-mt-32 standard-space-below" data-test="bottom-caption" id="video-description-4Hw9ygQ9pooEpcH71cZEii"><p>Supplementary file 3 (mp4 14164 KB)</p></div></div><div id="MOESM3"><div class="video" id="mijsvdivAfbQjAFjVD77UmJy7DNaja"><div mi24-video-player="true" video-id="AfbQjAFjVD77UmJy7DNaja" player-id="8PcXmCm9nWqE6posBEkd1h" config-type="vmpro" flash-path="//e.video-cdn.net/v2/" api-url="//d.video-cdn.net/play"></div><script src="//e.video-cdn.net/v2/embed.js"></script></div><div class="serif u-mb-0 u-mt-32 standard-space-below" data-test="bottom-caption" id="video-description-AfbQjAFjVD77UmJy7DNaja"><p>Supplementary file 4 (mp4 42211 KB)</p></div></div></div></div></div></section><section data-title="Rights and permissions"><div class="c-article-section" id="rightslink-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="rightslink">Rights and permissions</h2><div class="c-article-section__content" id="rightslink-content"> <p><b>Open Access</b> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 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id="citeas">Cite this article</h3><p class="c-bibliographic-information__citation">Shneor, R., Naveh, G., Ben-David, S. <i>et al.</i> A planning and execution framework for robotic assembly with deformable objects using a centralized database based on the Rαβγ categorization. <i>J Intell Manuf</i> (2025). https://doi.org/10.1007/s10845-025-02578-5</p><p class="c-bibliographic-information__download-citation u-hide-print"><a data-test="citation-link" data-track="click" data-track-action="download article citation" data-track-label="link" data-track-external="" rel="nofollow" href="https://citation-needed.springer.com/v2/references/10.1007/s10845-025-02578-5?format=refman&flavour=citation">Download citation<svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-download-medium"></use></svg></a></p><ul class="c-bibliographic-information__list" data-test="publication-history"><li 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