基于数字孪生的人机协作环境自适应分配方法

IF 2.4 3区 工程技术 Q3 ENGINEERING, MANUFACTURING
Xin Ma, Qinglin Qi, Fei Tao
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引用次数: 0

摘要

摘要:人机协作是人与机器人最佳技能的结合,在满足各领域安全、高效、灵活的需求方面显示出广阔的应用前景。人与机器人之间更紧密互动的理念极大地推动了数字孪生的探索,以加强协作。通过提供高保真模型和实时物理-虚拟交互,数字孪生能够实现对物理场景的准确反映,不仅包括人机条件,还包括环境变化。然而,不可预测事件的出现可能会导致既定计划与实际执行之间的不一致。针对这一问题,本文提出了一种基于数字孪生的人机协作环境自适应分配方法。该方法包括一个因素-事件-行为机制,从数字孪生体的内部和外部角度分析动态事件及其影响,以及一个基于遗传算法的分配算法来响应这些事件。最后进行了实验,旨在证明所提出方法的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Digital Twin-based environment-adaptive assignment method for human-robot collaboration
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.
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来源期刊
CiteScore
6.80
自引率
20.00%
发文量
126
审稿时长
12 months
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
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