Virtual reality in manufacturing: immersive and collaborative artificial-reality in design of human-robot workspace-Reference-Cited by-同舟云学术

Virtual reality in manufacturing: immersive and collaborative artificial-reality in design of human-robot workspace

Published:2019-11-26 Issue:1 Volume:33 Page:22-37
ISSN:0951-192X
Container-title:International Journal of Computer Integrated Manufacturing
language:en
Short-container-title:International Journal of Computer Integrated Manufacturing

Author:

Malik Ali Ahmad¹,Masood Tariq²,Bilberg Arne¹

Affiliation:

1. Mads Clausen Institute, University of Southern Denmark, Sønderborg, Denmark

2. Department of Engineering, University of Cambridge, Cambridge, UK

Publisher

Informa UK Limited

Subject

Electrical and Electronic Engineering,Computer Science Applications,Mechanical Engineering,Aerospace Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/0951192X.2019.1690685

Reference48 articles.

1. Putting VR/AR to Work

2. Anthes, C., R. J. Garcia-Hernández, M. Wiedemann, and D. Kranzlmüller. 2016. “State of the Art of Virtual Reality Technology.” IEEE Aerospace Conference, M.T., U.S.A, 1–19.

3. Begault, D. R., and L. J. Trejo. 2000. “3-D Sound for Virtual Reality and Multimedia.” National Aeronautics and Space Administration. NASA/TM-2000-209606.

4. Digital twin driven human–robot collaborative assembly

Cited by 119 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Exploring the synergies between collaborative robotics, digital twins, augmentation, and industry 5.0 for smart manufacturing: A state-of-the-art review;Robotics and Computer-Integrated Manufacturing;2024-10

2. Industrial metaverse towards Industry 5.0: Connotation, architecture, enablers, and challenges;Journal of Manufacturing Systems;2024-10

3. Key technologies and research progress in robotic arc additive remanufacturing;Sensors and Actuators A: Physical;2024-10

4. Human-Robot Collaboration in Mixed-Flow Assembly Line Balancing under Uncertainty: An Efficient Discrete Bees Algorithm;Journal of Industrial Information Integration;2024-09

5. ConIQA: A deep learning method for perceptual image quality assessment with limited data;Scientific Reports;2024-08-29