{"title":"一种基于视觉的人机自适应协作数字孪生建模方法","authors":"Junming Fan, Pai Zheng, Carman K. M. Lee","doi":"10.1115/1.4062430","DOIUrl":null,"url":null,"abstract":"\n Human-robot collaboration (HRC) has been regarded as one of the most promising paradigms for human-centric smart manufacturing in the context of Industry 5.0. To improve human well-being and robotic flexibility in HRC, a plethora of works around human body perception have emerged over the years, but most of them only considered a specific facade of human recognition while lacking a holistic perspective of the human operator. To this end, this study proposes an exemplary vision-based Human Digital Twin (HDT) model for highly dynamic HRC applications. The model mainly consists of a convolutional neural network that can simultaneously model the hierarchical human status including 3D human posture, action intention, and ergonomic risk. Then, on the basis of the constructed HDT, a robotic motion planning strategy is further introduced with the aim of adaptively optimizing the robotic motion trajectory. Further experiments and case studies are conducted in an HRC scenario to demonstrate the effectiveness of our approach.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Vision-based Human Digital Twin Modelling Approach for Adaptive Human-Robot Collaboration\",\"authors\":\"Junming Fan, Pai Zheng, Carman K. M. Lee\",\"doi\":\"10.1115/1.4062430\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Human-robot collaboration (HRC) has been regarded as one of the most promising paradigms for human-centric smart manufacturing in the context of Industry 5.0. To improve human well-being and robotic flexibility in HRC, a plethora of works around human body perception have emerged over the years, but most of them only considered a specific facade of human recognition while lacking a holistic perspective of the human operator. To this end, this study proposes an exemplary vision-based Human Digital Twin (HDT) model for highly dynamic HRC applications. The model mainly consists of a convolutional neural network that can simultaneously model the hierarchical human status including 3D human posture, action intention, and ergonomic risk. Then, on the basis of the constructed HDT, a robotic motion planning strategy is further introduced with the aim of adaptively optimizing the robotic motion trajectory. Further experiments and case studies are conducted in an HRC scenario to demonstrate the effectiveness of our approach.\",\"PeriodicalId\":16299,\"journal\":{\"name\":\"Journal of Manufacturing Science and Engineering-transactions of The Asme\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2023-04-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Manufacturing Science and Engineering-transactions of The Asme\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1115/1.4062430\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Manufacturing Science and Engineering-transactions of The Asme","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1115/1.4062430","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}
A Vision-based Human Digital Twin Modelling Approach for Adaptive Human-Robot Collaboration
Human-robot collaboration (HRC) has been regarded as one of the most promising paradigms for human-centric smart manufacturing in the context of Industry 5.0. To improve human well-being and robotic flexibility in HRC, a plethora of works around human body perception have emerged over the years, but most of them only considered a specific facade of human recognition while lacking a holistic perspective of the human operator. To this end, this study proposes an exemplary vision-based Human Digital Twin (HDT) model for highly dynamic HRC applications. The model mainly consists of a convolutional neural network that can simultaneously model the hierarchical human status including 3D human posture, action intention, and ergonomic risk. Then, on the basis of the constructed HDT, a robotic motion planning strategy is further introduced with the aim of adaptively optimizing the robotic motion trajectory. Further experiments and case studies are conducted in an HRC scenario to demonstrate the effectiveness of our approach.
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