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Real-time Human Motion Estimation for Human Robot Collaboration
Kang J(康杰)1,2; Jia K(贾凯)1,3; Xu F(徐方)1,3; Zou FS(邹风山)1,3; Zhang YA(张延安)1,2; Ren HL(任恒乐)1,2
Department其他
Conference Name2018 IEEE 8th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems
Conference DateJuly 19-23, 2018
Conference PlaceTianjin, China
Author of SourceIEEE Robotics & Automation Society
Source PublicationProceedings of 2018 IEEE 8th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems
PublisherIEEE
Publication PlaceNew York
2018
Pages552-557
Indexed ByEI ; CPCI(ISTP)
EI Accession number20191806865295
WOS IDWOS:000468941800097
Contribution Rank1
ISSN2379-7711
ISBN978-1-5386-7056-9
Keywordhuman robot collaboration GMM EM real-time motion estimation minimum jerk
AbstractIn the process of human robot collaboration, safety is of vital importance, especially when the workspaces of human and robot are intersected, and collisions between them should be avoided. To avoid collision accurately, the motion of people must be in charge in real time, and making a reasonable estimate of human motion, so that the robots can make decisions accordingly, and plan their own motion quickly. This paper presents a framework of real-time motion estimation based on human posture which is based on ROS, firstly, the position of human joints is collected through the Kinect, then the gaussian mixture model (GMM) algorithm and EM algorithm are used to cluster and estimate based on the collected coordinate points, and adding labels to each category, which can help get the sequence of the joint, and realize the function of motion estimation. To guarantee the safety of people, this paper also discusses the motion estimation method of human motion trajectory mutation, which avoids the collision in case of emergency. Finally, the experimental results show that the presented framework of real-time motion estimation can describe the human body's movement accurately and make an accurate prediction, not only ensuring the human security, and it's of great significance in improving the production efficiency.
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/24682
Collection其他
Corresponding AuthorKang J(康杰)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
3.Shenyang SIASUN Robot & Automation Co., LTD., China, Shenyang 110168, China
Recommended Citation
GB/T 7714
Kang J,Jia K,Xu F,et al. Real-time Human Motion Estimation for Human Robot Collaboration[C]//IEEE Robotics & Automation Society. New York:IEEE,2018:552-557.
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