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Drivable Space of Rehabilitation Robot for Physical Human-Robot Interaction: Definition and an Expanding Method
Wang, Weiqun1,2; Liang, Xu3; Liu, Shengda1; Lin, Tianyu1,2; Zhang, Pu5; Lv, Zhen5; Wang, Jiaxing1; Hou, Zeng-Guang1,2,4
发表期刊IEEE TRANSACTIONS ON ROBOTICS
ISSN1552-3098
2022-08-26
页码14
通讯作者Wang, Weiqun(weiqun.wang@ia.ac.cn) ; Hou, Zeng-Guang(zengguang.hou@ia.ac.cn)
摘要Physical human-robot interaction performance of present rehabilitation robots are still not satisfactory in the clinical practice. Especially, the work space where the robot can be driven smoothly by users is still very limited, which prevents rehabilitation robots from being applied successfully. In this study, a new concept of drivable space is proposed to evaluate the work spaces of rehabilitation robots, and a method for expanding the drivable space is designed based on the dynamics of the coupled human-robot system and human joint characteristics. First, the definition of drivable space is presented based on comparison of human joint torques, and the minimal torques necessary to drive robot joints, which is mainly determined by the torque estimation errors for general rehabilitation robots driven smoothly by motors. Therefore, a method for improving torque estimation accuracies based on dynamics modeling is then designed. A data-driven error prediction method based on Gaussian process regression is proposed to adaptively compensate the model errors, by which the most accurate dynamic model so far for the coupled system can be obtained, and a method for generation of the training dataset, which is used in error prediction, is designed as well. Moreover, the torque-angle relationship of human joints is modeled and used to optimize the torque error distribution, by which it can be proven that the drivable space can be further expanded. Finally, performance of the proposed methods are demonstrated and validated by experiments carried out on a lower limb rehabilitation robot.
关键词Assistive robots Torque Robot kinematics Training Exoskeletons Biological system modeling Adaptation models Adaptive learning dynamics modeling physical human-robot interaction (pHRI) rehabilitation robot
DOI10.1109/TRO.2022.3189231
关键词[WOS]IDENTIFICATION ; COMPENSATION ; FRICTION ; MODEL
收录类别SCI
语种英语
资助项目National Natural Science Foundation of China[U1913601] ; National Natural Science Foundation of China[61720106012] ; Beijing SciTech Program[Z211100007921021] ; Beijing SciTech Program[4202074] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32040000]
项目资助者National Natural Science Foundation of China ; Beijing SciTech Program ; Strategic Priority Research Program of Chinese Academy of Science
WOS研究方向Robotics
WOS类目Robotics
WOS记录号WOS:000849230300001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
被引频次:2[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/50072
专题复杂系统认知与决策实验室_先进机器人
通讯作者Wang, Weiqun; Hou, Zeng-Guang
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.North China Univ Technol, Dept Mech & Elect Engn, Beijing 100144, Peoples R China
4.Macau Univ Sci & Technol, Inst Syst Engn, CASIA Must Joint Lab Intelligence Sci & Technol, Macau 999078, Peoples R China
5.China Rehabil Res Ctr, Beijing 100068, Peoples R China
第一作者单位中国科学院自动化研究所
通讯作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Wang, Weiqun,Liang, Xu,Liu, Shengda,et al. Drivable Space of Rehabilitation Robot for Physical Human-Robot Interaction: Definition and an Expanding Method[J]. IEEE TRANSACTIONS ON ROBOTICS,2022:14.
APA Wang, Weiqun.,Liang, Xu.,Liu, Shengda.,Lin, Tianyu.,Zhang, Pu.,...&Hou, Zeng-Guang.(2022).Drivable Space of Rehabilitation Robot for Physical Human-Robot Interaction: Definition and an Expanding Method.IEEE TRANSACTIONS ON ROBOTICS,14.
MLA Wang, Weiqun,et al."Drivable Space of Rehabilitation Robot for Physical Human-Robot Interaction: Definition and an Expanding Method".IEEE TRANSACTIONS ON ROBOTICS (2022):14.
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