Academic Journal
Assembly task planning framework based on knowledge graph.
| Τίτλος: | Assembly task planning framework based on knowledge graph. |
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| Συγγραφείς: | Xu, Zhaobo, Zhang, Chaoran, Hou, Sheng, Han, Zhaochun, Zeng, Long, Feng, Pingfa |
| Πηγή: | Journal of Intelligent Manufacturing; Sep2026, Vol. 37 Issue 9, p3481-3504, 24p |
| Θεματικοί όροι: | Knowledge graphs, Flexible manufacturing systems, Work design, Industrial robots, Program generators (Computer programs), Task analysis |
| Περίληψη: | Assembly task planning (ATP) translates natural language described assembly tasks into executable programs, along with desired product models as input. It is a critical component of embodied intelligent assembly systems. However, current methods leveraging large language models are unable to comply with industrial standards and constraints well. We address this issue by proposing a knowledge graph (KG) based ATP framework that comprises four key modules: assembly KG, high-level task planner, low-level skill controller and reconfigurable flexible assembly system. First, the information of a product family is stored in an assembly KG. Then, a given language described assembly task is decomposed into a sequence of subtasks by the high-level task planner based on product information and inference rules in the assembly KG. After that, the low-level skill controller further transforms the assembly subtasks into skills that can be ultimately mapped to assembly programs, which can be directly executed on our highly reconfigurable flexible assembly system. Moreover, we develop a software platform to ease the assembly program generation. The proposed method is demonstrated by its application to the assembly of four valves. The results demonstrate that the programming time has been reduced from 10 to 25 min to less than 1 s. And the assembly accuracy can reach over 80%. These show the effectiveness and potential of our KG-based ATP framework in improving efficiency and reducing manual workload in industrial assembly scenarios. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Intelligent Manufacturing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1007/s10845-025-02695-1 Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
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| Header | DbId: edb DbLabel: Complementary Index An: 196029113 RelevancyScore: 1082 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1082.42749023438 |
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| Items | – Name: Title Label: Title Group: Ti Data: Assembly task planning framework based on knowledge graph. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Xu%2C+Zhaobo%22">Xu, Zhaobo</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Chaoran%22">Zhang, Chaoran</searchLink><br /><searchLink fieldCode="AR" term="%22Hou%2C+Sheng%22">Hou, Sheng</searchLink><br /><searchLink fieldCode="AR" term="%22Han%2C+Zhaochun%22">Han, Zhaochun</searchLink><br /><searchLink fieldCode="AR" term="%22Zeng%2C+Long%22">Zeng, Long</searchLink><br /><searchLink fieldCode="AR" term="%22Feng%2C+Pingfa%22">Feng, Pingfa</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Intelligent Manufacturing; Sep2026, Vol. 37 Issue 9, p3481-3504, 24p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Knowledge+graphs%22">Knowledge graphs</searchLink><br /><searchLink fieldCode="DE" term="%22Flexible+manufacturing+systems%22">Flexible manufacturing systems</searchLink><br /><searchLink fieldCode="DE" term="%22Work+design%22">Work design</searchLink><br /><searchLink fieldCode="DE" term="%22Industrial+robots%22">Industrial robots</searchLink><br /><searchLink fieldCode="DE" term="%22Program+generators+%28Computer+programs%29%22">Program generators (Computer programs)</searchLink><br /><searchLink fieldCode="DE" term="%22Task+analysis%22">Task analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Assembly task planning (ATP) translates natural language described assembly tasks into executable programs, along with desired product models as input. It is a critical component of embodied intelligent assembly systems. However, current methods leveraging large language models are unable to comply with industrial standards and constraints well. We address this issue by proposing a knowledge graph (KG) based ATP framework that comprises four key modules: assembly KG, high-level task planner, low-level skill controller and reconfigurable flexible assembly system. First, the information of a product family is stored in an assembly KG. Then, a given language described assembly task is decomposed into a sequence of subtasks by the high-level task planner based on product information and inference rules in the assembly KG. After that, the low-level skill controller further transforms the assembly subtasks into skills that can be ultimately mapped to assembly programs, which can be directly executed on our highly reconfigurable flexible assembly system. Moreover, we develop a software platform to ease the assembly program generation. The proposed method is demonstrated by its application to the assembly of four valves. The results demonstrate that the programming time has been reduced from 10 to 25 min to less than 1 s. And the assembly accuracy can reach over 80%. These show the effectiveness and potential of our KG-based ATP framework in improving efficiency and reducing manual workload in industrial assembly scenarios. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Intelligent Manufacturing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10845-025-02695-1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 3481 Subjects: – SubjectFull: Knowledge graphs Type: general – SubjectFull: Flexible manufacturing systems Type: general – SubjectFull: Work design Type: general – SubjectFull: Industrial robots Type: general – SubjectFull: Program generators (Computer programs) Type: general – SubjectFull: Task analysis Type: general Titles: – TitleFull: Assembly task planning framework based on knowledge graph. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Xu, Zhaobo – PersonEntity: Name: NameFull: Zhang, Chaoran – PersonEntity: Name: NameFull: Hou, Sheng – PersonEntity: Name: NameFull: Han, Zhaochun – PersonEntity: Name: NameFull: Zeng, Long – PersonEntity: Name: NameFull: Feng, Pingfa IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 09565515 Numbering: – Type: volume Value: 37 – Type: issue Value: 9 Titles: – TitleFull: Journal of Intelligent Manufacturing Type: main |
| ResultId | 1 |