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Data-driven modelling of human-human co-manipulation using force and muscle surface electromyogram activities
With collaborative robots and the recent developments in manufacturing technologies, physical interactions between humans and robots represent a vital role in performing collaborative tasks. Most previous studies have focused on robot motion planning and control during the execution of the task. How...
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| Main Authors: | , , , , , , |
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| Format: | Default Article |
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2021
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| Online Access: | https://hdl.handle.net/2134/14822616.v1 |
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| _version_ | 1805987373842956288 |
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| author | Ali Al-Yacoub Myles Flanagan Achim Buerkle Thomas Bamber Pedro Ferreira Ella-Mae Hubbard Niels Lohse |
| author_facet | Ali Al-Yacoub Myles Flanagan Achim Buerkle Thomas Bamber Pedro Ferreira Ella-Mae Hubbard Niels Lohse |
| author_sort | Ali Al-Yacoub (4101397) |
| collection | Figshare |
| description | With collaborative robots and the recent developments in manufacturing technologies, physical interactions between humans and robots represent a vital role in performing collaborative tasks. Most previous studies have focused on robot motion planning and control during the execution of the task. However, further research is required for direct physical contact for human-robot or robot-robot interactions, such as co-manipulation. In co-manipulation, a human operator manipulates a shared load with a robot through a semi-structured environment. In such scenarios, a multi-contact point with the environment during the task execution results in a convoluted force/toque signature that is difficult to interpret. Therefore, in this paper, a muscle activity sensor in the form of an electromyograph (EMG) is employed to improve the mapping between force/torque and displacements in co-manipulation tasks. A suitable mapping was identified by comparing the root mean square error amongst data-driven models, mathematical models, and hybrid models. Thus, a robot was shown to effectively and naturally perform the required co-manipulation with a human. This paper’s proposed hypotheses were validated using an unseen test dataset and a simulated co-manipulation experiment, which showed that the EMG and data-driven model improved the mapping of the force/torque features into displacements. |
| format | Default Article |
| id | rr-article-14822616 |
| institution | Loughborough University |
| publishDate | 2021 |
| record_format | Figshare |
| spelling | rr-article-148226162021-06-22T00:00:00Z Data-driven modelling of human-human co-manipulation using force and muscle surface electromyogram activities Ali Al-Yacoub (4101397) Myles Flanagan (6036455) Achim Buerkle (5781074) Thomas Bamber (1402396) Pedro Ferreira (1253733) Ella-Mae Hubbard (1252581) Niels Lohse (1251180) human-robot collaboration human-human co-manipulation data-driven modelling mathematical modelling object manipulation impedance control With collaborative robots and the recent developments in manufacturing technologies, physical interactions between humans and robots represent a vital role in performing collaborative tasks. Most previous studies have focused on robot motion planning and control during the execution of the task. However, further research is required for direct physical contact for human-robot or robot-robot interactions, such as co-manipulation. In co-manipulation, a human operator manipulates a shared load with a robot through a semi-structured environment. In such scenarios, a multi-contact point with the environment during the task execution results in a convoluted force/toque signature that is difficult to interpret. Therefore, in this paper, a muscle activity sensor in the form of an electromyograph (EMG) is employed to improve the mapping between force/torque and displacements in co-manipulation tasks. A suitable mapping was identified by comparing the root mean square error amongst data-driven models, mathematical models, and hybrid models. Thus, a robot was shown to effectively and naturally perform the required co-manipulation with a human. This paper’s proposed hypotheses were validated using an unseen test dataset and a simulated co-manipulation experiment, which showed that the EMG and data-driven model improved the mapping of the force/torque features into displacements. 2021-06-22T00:00:00Z Text Journal contribution 2134/14822616.v1 https://figshare.com/articles/journal_contribution/Data-driven_modelling_of_human-human_co-manipulation_using_force_and_muscle_surface_electromyogram_activities/14822616 CC BY 4.0 |
| spellingShingle | human-robot collaboration human-human co-manipulation data-driven modelling mathematical modelling object manipulation impedance control Ali Al-Yacoub Myles Flanagan Achim Buerkle Thomas Bamber Pedro Ferreira Ella-Mae Hubbard Niels Lohse Data-driven modelling of human-human co-manipulation using force and muscle surface electromyogram activities |
| title | Data-driven modelling of human-human co-manipulation using force and muscle surface electromyogram activities |
| title_full | Data-driven modelling of human-human co-manipulation using force and muscle surface electromyogram activities |
| title_fullStr | Data-driven modelling of human-human co-manipulation using force and muscle surface electromyogram activities |
| title_full_unstemmed | Data-driven modelling of human-human co-manipulation using force and muscle surface electromyogram activities |
| title_short | Data-driven modelling of human-human co-manipulation using force and muscle surface electromyogram activities |
| title_sort | data-driven modelling of human-human co-manipulation using force and muscle surface electromyogram activities |
| topic | human-robot collaboration human-human co-manipulation data-driven modelling mathematical modelling object manipulation impedance control |
| url | https://hdl.handle.net/2134/14822616.v1 |