{"title":"A Digital Twin-based environment-adaptive assignment method for human-robot collaboration","authors":"Xin Ma, Qinglin Qi, Fei Tao","doi":"10.1115/1.4064040","DOIUrl":null,"url":null,"abstract":"Abstract Human-robot collaboration, which strives to combine the best skills of humans and robots, has shown board application prospects in meeting safe-effective-flexible requirements in various fields. The ideation of much closer interaction between humans and robots has greatly developed the exploration of digital twin to enhance the collaboration. By offering high-fidelity models and real-time physical-virtual interaction, digital twin enables to achieve an accurate reflection of the physical scenario, including not only human-robot conditions but also environment changes. However, the appearance of unpredictable events may cause an inconsistency between the established schedule and actual execution. To cope with this issue, an environment-adaptive assignment method based on digital twin for human-robot collaboration is formed in this study. The proposed approach is consisted of a factor-event-act mechanism that analyzes the dynamic events and their impacts from both internal and external perspectives of the digital twin, and a GA-based assignment algorithm to response to them. Experiments are carried out in the last part, aiming to show the feasibility of the proposed method.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":"98 s1","pages":"0"},"PeriodicalIF":2.4000,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Manufacturing Science and Engineering-transactions of The Asme","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/1.4064040","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract Human-robot collaboration, which strives to combine the best skills of humans and robots, has shown board application prospects in meeting safe-effective-flexible requirements in various fields. The ideation of much closer interaction between humans and robots has greatly developed the exploration of digital twin to enhance the collaboration. By offering high-fidelity models and real-time physical-virtual interaction, digital twin enables to achieve an accurate reflection of the physical scenario, including not only human-robot conditions but also environment changes. However, the appearance of unpredictable events may cause an inconsistency between the established schedule and actual execution. To cope with this issue, an environment-adaptive assignment method based on digital twin for human-robot collaboration is formed in this study. The proposed approach is consisted of a factor-event-act mechanism that analyzes the dynamic events and their impacts from both internal and external perspectives of the digital twin, and a GA-based assignment algorithm to response to them. Experiments are carried out in the last part, aiming to show the feasibility of the proposed method.
期刊介绍:
Areas of interest including, but not limited to: Additive manufacturing; Advanced materials and processing; Assembly; Biomedical manufacturing; Bulk deformation processes (e.g., extrusion, forging, wire drawing, etc.); CAD/CAM/CAE; Computer-integrated manufacturing; Control and automation; Cyber-physical systems in manufacturing; Data science-enhanced manufacturing; Design for manufacturing; Electrical and electrochemical machining; Grinding and abrasive processes; Injection molding and other polymer fabrication processes; Inspection and quality control; Laser processes; Machine tool dynamics; Machining processes; Materials handling; Metrology; Micro- and nano-machining and processing; Modeling and simulation; Nontraditional manufacturing processes; Plant engineering and maintenance; Powder processing; Precision and ultra-precision machining; Process engineering; Process planning; Production systems optimization; Rapid prototyping and solid freeform fabrication; Robotics and flexible tooling; Sensing, monitoring, and diagnostics; Sheet and tube metal forming; Sustainable manufacturing; Tribology in manufacturing; Welding and joining