Hand gesture teleoperation for dexterous manipulators in space station by using monocular hand motion capture

被引:20
作者
Gao, Qing [1 ,2 ,3 ]
Li, Jinyang [2 ,4 ]
Zhu, Yimin [2 ,4 ]
Wang, Siyue [2 ,5 ]
Liufu, Jingshu [2 ,6 ]
Liu, Jinguo [2 ,3 ]
机构
[1] Sun Yat Sen Univ, Sch Elect & Commun Engn, Shenzhen 518107, Peoples R China
[2] Chinese Acad Sci, Shenyang Inst Automat, State Key Lab Robot, Shenyang 110016, Peoples R China
[3] Chinese Acad Sci, Inst Robot & Intelligent Mfg, Shenyang 110016, Peoples R China
[4] Shenyang Univ Chem Technol, Shenyang 110142, Peoples R China
[5] Shenyang Ligong Univ, Shenyang 110158, Peoples R China
[6] Univ Sci & Technol Liaoning, Shenyang 110325, Peoples R China
关键词
Hand pose estimation; Teleoperation; Space robot; Dexterous manipulator; ROBOT;
D O I
10.1016/j.actaastro.2022.11.047
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
Space robots in the space station can assist or substitute astronauts to conduct some in-cabin tasks to alleviate the problems of heavy tasks and the limited number of astronauts in the space station. In response to these problems, a hand gesture teleoperation method is proposed for controlling of dexterous manipulators in the space station, which is based on monocular hand motion capture and video-calling tools. This method can realize free and convenient teleoperation. The work is mainly divided into two parts, which are monocular hand motion capture and teleoperation framework for dexterous manipulators. For monocular hand motion capture, a hand pose estimation method based on hand biological constraints from single RGB images is designed. On the other hand, a dexterous manipulator teleoperation framework based on hand motion capture and video -calling tools is designed. Experimental results carried out on hand pose estimation datasets proved that the proposed hand pose estimation method outperforms the state-of-the-art methods. Physical experiment on a space dexterous manipulator teleoperation platform validated the effectiveness of the proposed hand gesture teleoperation method.
引用
收藏
页码:630 / 639
页数:10
相关论文
共 43 条
[1]   Pushing the Envelope for RGB-based Dense 3D Hand Pose Estimation via Neural Rendering [J].
Baek, Seungryul ;
Kim, Kwang In ;
Kim, Tae-Kyun .
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019), 2019, :1067-1076
[2]   3D Hand Shape and Pose from Images in the Wild [J].
Boukhayma, Adnane ;
de Bem, Rodrigo ;
Torr, Philip H. S. .
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019), 2019, :10835-10844
[3]  
Bridgwater LB, 2012, IEEE INT CONF ROBOT, P3425, DOI 10.1109/ICRA.2012.6224772
[4]  
Bualat M, 2015, AIAA SPACE 2015 Conference and Exposition, P4643, DOI DOI 10.2514/6.2015-4643
[5]  
Chen Z., 2021, P IEEECVF INT C COMP, P11626
[6]   Approach and maneuver for failed spacecraft de-tumbling via space teleoperation robot system [J].
Cheng, Ruizhou ;
Liu, Zhengxiong ;
Ma, Zhiqiang ;
Huang, Panfeng .
ACTA ASTRONAUTICA, 2021, 181 :384-395
[7]  
Diftler MA, 2011, IEEE INT CONF ROBOT, P2178
[8]   Dynamic Hand Gesture Recognition Based on Signals From Specialized Data Glove and Deep Learning Algorithms [J].
Dong, Yongfeng ;
Liu, Jielong ;
Yan, Wenjie .
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2021, 70
[9]   JGR-P2O: Joint Graph Reasoning Based Pixel-to-Offset Prediction Network for 3D Hand Pose Estimation from a Single Depth Image [J].
Fang, Linpu ;
Liu, Xingyan ;
Liu, Li ;
Xu, Hang ;
Kang, Wenxiong .
COMPUTER VISION - ECCV 2020, PT VI, 2020, 12351 :120-137
[10]  
Gao Q., 2021, IEEE SENS J