@inproceedings{379ba969400a4ac1ace4fe8a7184fb84,
title = "Research on Human-Machine Task Collaboration Based on Action Recognition",
abstract = "A human-machine collaboration method based on real time action recognition is presented to achieve the collaboration between automated guided vehicles (AGVs) and human operators through the perception of tasks conducted by human operators. The process of action recognition is accomplished by a Kinect vision sensor and convolutional neural networks (CNN), and the methods of multiple features fusion and hierarchical cluster are applied in the process of action recognition. The utilization of action recognition technology can effectively shorten the expected waiting time, and can avoid the assembly line stagnation caused by human errors to a great extent. Finally, the experiment result indicates that the overall efficiency has been increased significantly after applying the method.",
keywords = "action recognition, human error, human-machine collaboration, hybrid assembly line, time allowance",
author = "Jihong Yan and Shenyi Yan and Lizhong Zhao and Zipeng Wang and Yun Liang",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Smart Manufacturing, Industrial and Logistics Engineering, SMILE 2019 ; Conference date: 20-04-2019 Through 21-04-2019",
year = "2019",
month = apr,
doi = "10.1109/SMILE45626.2019.8965279",
language = "英语",
series = "Proceedings - 2019 IEEE International Conference on Smart Manufacturing, Industrial and Logistics Engineering, SMILE 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "117--121",
editor = "Chen-Fu Chien and Renzhong Tang and Runliang Dou and Jei-Zheng Wu",
booktitle = "Proceedings - 2019 IEEE International Conference on Smart Manufacturing, Industrial and Logistics Engineering, SMILE 2019",
address = "美国",
}