A Vision-based Human Digital Twin Modelling Approach for Adaptive Human-Robot Collaboration

IF 2.4 3区 工程技术 Q3 ENGINEERING, MANUFACTURING Journal of Manufacturing Science and Engineering-transactions of The Asme Pub Date : 2023-04-27 DOI:10.1115/1.4062430
Junming Fan, Pai Zheng, Carman K. M. Lee
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引用次数: 1

Abstract

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.
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一种基于视觉的人机自适应协作数字孪生建模方法
人机协作(HRC)被认为是工业5.0背景下以人为中心的智能制造最有前途的范例之一。为了提高HRC中的人类福祉和机器人灵活性,多年来出现了大量围绕人体感知的工作,但其中大多数只考虑了人类识别的特定外观,而缺乏人类操作员的整体视角。为此,本研究提出了一个典型的基于视觉的人类数字孪生(HDT)模型,用于高度动态的HRC应用。该模型主要由卷积神经网络组成,该网络可以同时对三维人体姿态、动作意图和人体工程学风险等分层人体状态进行建模。然后,在构建的HDT基础上,进一步引入机器人运动规划策略,以自适应优化机器人运动轨迹。在HRC场景中进行了进一步的实验和案例研究,以证明我们的方法的有效性。
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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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