Bayesian Estimation of Human Impedance and Motion Intention for Human-Robot Collaboration
This article proposes a Bayesian method to acquire the estimation of human impedance and motion intention in a human-robot collaborative task. Combining with the prior knowledge of human stiffness, estimated stiffness obeying Gaussian distribution is obtained by Bayesian estimation, and human motion...
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| Veröffentlicht in: | IEEE transactions on cybernetics 2021-04, Vol.51 (4), p.1822-1834 |
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| container_title | IEEE transactions on cybernetics |
| container_volume | 51 |
| creator | Yu, Xinbo He, Wei Li, Yanan Xue, Chengqian Li, Jianqiang Zou, Jianxiao Yang, Chenguang |
| description | This article proposes a Bayesian method to acquire the estimation of human impedance and motion intention in a human-robot collaborative task. Combining with the prior knowledge of human stiffness, estimated stiffness obeying Gaussian distribution is obtained by Bayesian estimation, and human motion intention can be also estimated. An adaptive impedance control strategy is employed to track a target impedance model and neural networks are used to compensate for uncertainties in robotic dynamics. Comparative simulation results are carried out to verify the effectiveness of estimation method and emphasize the advantages of the proposed control strategy. The experiment, performed on Baxter robot platform, illustrates a good system performance. |
| doi_str_mv | 10.1109/TCYB.2019.2940276 |
| format | Article |
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Combining with the prior knowledge of human stiffness, estimated stiffness obeying Gaussian distribution is obtained by Bayesian estimation, and human motion intention can be also estimated. An adaptive impedance control strategy is employed to track a target impedance model and neural networks are used to compensate for uncertainties in robotic dynamics. Comparative simulation results are carried out to verify the effectiveness of estimation method and emphasize the advantages of the proposed control strategy. 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(IEEE) 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c458t-55831898e76023df9b6d2e48cc4458bef02f331e19ceee049dc6a2115112e2a33</citedby><cites>FETCH-LOGICAL-c458t-55831898e76023df9b6d2e48cc4458bef02f331e19ceee049dc6a2115112e2a33</cites><orcidid>0000-0002-1443-2547 ; 0000-0002-8676-8322 ; 0000-0002-8944-9861 ; 0000-0002-2208-962X ; 0000-0001-5255-5559 ; 0000-0002-6788-0634</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8879539$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8879539$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31647450$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Yu, Xinbo</creatorcontrib><creatorcontrib>He, Wei</creatorcontrib><creatorcontrib>Li, Yanan</creatorcontrib><creatorcontrib>Xue, Chengqian</creatorcontrib><creatorcontrib>Li, Jianqiang</creatorcontrib><creatorcontrib>Zou, Jianxiao</creatorcontrib><creatorcontrib>Yang, Chenguang</creatorcontrib><title>Bayesian Estimation of Human Impedance and Motion Intention for Human-Robot Collaboration</title><title>IEEE transactions on cybernetics</title><addtitle>TCYB</addtitle><addtitle>IEEE Trans Cybern</addtitle><description>This article proposes a Bayesian method to acquire the estimation of human impedance and motion intention in a human-robot collaborative task. 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The experiment, performed on Baxter robot platform, illustrates a good system performance.</description><subject>Adaptive control</subject><subject>Adaptive impedance control</subject><subject>Bayes methods</subject><subject>Bayesian analysis</subject><subject>Bayesian estimation</subject><subject>Collaboration</subject><subject>Dynamics</subject><subject>Estimation</subject><subject>Force</subject><subject>Gaussian distribution</subject><subject>human impedance</subject><subject>Human motion</subject><subject>human motion intention estimation</subject><subject>Impedance</subject><subject>Neural networks</subject><subject>neural networks (NNs)</subject><subject>Normal distribution</subject><subject>Robot dynamics</subject><subject>Robots</subject><subject>Stiffness</subject><subject>Task analysis</subject><subject>Tracking</subject><issn>2168-2267</issn><issn>2168-2275</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkM9LwzAUx4Mobsz9ASJIwYuXzvxsk6Mr0w0mgszDTiFtX6FjbWbTHvbfm65zB3PJ473P--abL0L3BM8Iweplk2znM4qJmlHFMY2jKzSmJJIhpbG4vtRRPEJT53bYH-lbSt6iESMRj7nAY7SdmyO40tTBwrVlZdrS1oEtgmVX-d6qOkBu6gwCU-fBhz1NV3UL9akqbDOA4ZdNbRskdr83qW1OKnfopjB7B9PzPUHfb4tNsgzXn--r5HUdZlzINhRCMuJdQRxhyvJCpVFOgcss436eQoFpwRgBojIAwFzlWWQoIYIQCtQwNkHPg-6hsT8duFZXpcvAO6nBdk5ThqXwL4geffqH7mzX1N6dpgIzrlTMlafIQGWNda6BQh8an0xz1ATrPnrdR6_76PU5er_zeFbu0gryy8Zf0B54GIDS_-IyljL2vhT7BbK0hkA</recordid><startdate>20210401</startdate><enddate>20210401</enddate><creator>Yu, Xinbo</creator><creator>He, Wei</creator><creator>Li, Yanan</creator><creator>Xue, Chengqian</creator><creator>Li, Jianqiang</creator><creator>Zou, Jianxiao</creator><creator>Yang, Chenguang</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Adaptive control Adaptive impedance control Bayes methods Bayesian analysis Bayesian estimation Collaboration Dynamics Estimation Force Gaussian distribution human impedance Human motion human motion intention estimation Impedance Neural networks neural networks (NNs) Normal distribution Robot dynamics Robots Stiffness Task analysis Tracking |
| title | Bayesian Estimation of Human Impedance and Motion Intention for Human-Robot Collaboration |
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