Estimation of human impedance and motion intention for constrained human-robot interaction
文献类型：期刊论文
| 作者 | Yu, Xinbo1,2,3; Li, Yanan4; Zhang, Shuang1,2,3; Xue, Chengqian1,2,3; Wang, Yu5
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| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2020-05-21 |
| 卷号 | 390页码:268-279 |
| 关键词 | Human motion intention estimation Impedance learning Adaptive neural network control Full-state constraints Barrier Lyapunov functions |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2019.07.104 |
| 通讯作者 | Zhang, Shuang(zhangshuang.ac@gmail.com) |
| 英文摘要 | In this paper, a complete framework for safe and efficient physical human-robot interaction (pHRI) is developed for robot by considering both issues of adaptation to the human partner and ensuring the motion constraints during the interaction. We consider the robot's learning of not only human motion intention, but also the human impedance. We employ radial basis function neural networks (RBFNNs) to estimate human motion intention in real time, and least square method is utilized in robot learning of human impedance. When robot has learned the impedance information about human, it can adjust its desired impedance parameters by a simple tuning law for operative compliance. An adaptive impedance control integrated with RBFNNs and full-state constraints is also proposed in our work. We employ RBFNNs to compensate for uncertainties in the dynamics model of robot and barrier Lyapunov functions are chosen to ensure that full-state constraints are not violated in pHRI. Results in simulations and experiments show the better performance of our proposed framework compared with traditional methods. (C) 2019 Elsevier B.V. All rights reserved. |
| WOS关键词 | NEURAL-NETWORK CONTROL ; NONLINEAR-SYSTEMS ; TRACKING CONTROL ; ARM IMPEDANCE ; DESIGN |
| 资助项目 | National Natural Science Foundation of China[61873297] ; China Postdoctoral Science Foundation[2019T120048] ; China Postdoctoral Science Foundation[2018M630074] ; Fundamental Research Funds for the Central Universities[FRF-GF-18-027B] ; Beijing Science and Technology Project[Z181100003118006] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000531728800007 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; Fundamental Research Funds for the Central Universities ; Beijing Science and Technology Project |
| 源URL | [http://ir.ia.ac.cn/handle/173211/39464]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 自动化研究所_复杂系统管理与控制国家重点实验室 |
| 通讯作者 | Zhang, Shuang |
| 作者单位 | 1.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China 2.Univ Sci & Technol Beijing, Inst Artificial Intelligence, Beijing 100083, Peoples R China 3.Univ Sci & Technol Beijing, Key Lab Knowledge Automat Ind Proc, Minist Educ, Beijing 100083, Peoples R China 4.Univ Sussex, Dept Engn & Design, Brighton BN1 9RH, E Sussex, England 5.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yu, Xinbo,Li, Yanan,Zhang, Shuang,et al. Estimation of human impedance and motion intention for constrained human-robot interaction[J]. NEUROCOMPUTING,2020,390:268-279. |
| APA | Yu, Xinbo,Li, Yanan,Zhang, Shuang,Xue, Chengqian,&Wang, Yu.(2020).Estimation of human impedance and motion intention for constrained human-robot interaction.NEUROCOMPUTING,390,268-279. |
| MLA | Yu, Xinbo,et al."Estimation of human impedance and motion intention for constrained human-robot interaction".NEUROCOMPUTING 390(2020):268-279. |
入库方式： OAI收割
来源：自动化研究所
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