An Enhanced Robot Massage System in Smart Homes Using Force Sensing and a Dynamic Movement Primitive
With requirements to improve life quality, smart homes, and healthcare have gradually become a future lifestyle. In particular, service robots with human behavioral sensing for private or personal use in the home have attracted a lot of research attention thanks to their advantages in relieving high...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-06-01
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| Series: | Frontiers in Neurorobotics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fnbot.2020.00030/full |
| _version_ | 1818303502661713920 |
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| author | Chunxu Li Chunxu Li Ashraf Fahmy Ashraf Fahmy Shaoxiang Li Johann Sienz |
| author_facet | Chunxu Li Chunxu Li Ashraf Fahmy Ashraf Fahmy Shaoxiang Li Johann Sienz |
| author_sort | Chunxu Li |
| collection | DOAJ |
| description | With requirements to improve life quality, smart homes, and healthcare have gradually become a future lifestyle. In particular, service robots with human behavioral sensing for private or personal use in the home have attracted a lot of research attention thanks to their advantages in relieving high labor costs and the fatigue of human assistance. In this paper, a novel force-sensing- and robotic learning algorithm-based teaching interface for robot massaging has been proposed. For the teaching purposes, a human operator physically holds the end-effector of the robot to perform the demonstration. At this stage, the end position data are outputted and sent to be segmented via the Finite Difference (FD) method. A Dynamic Movement Primitive (DMP) is utilized to model and generalize the human-like movements. In order to learn from multiple demonstrations, Dynamic Time Warping (DTW) is used for the preprocessing of the data recorded on the robot platform, and a Gaussian Mixture Model (GMM) is employed for the evaluation of DMP to generate multiple patterns after the completion of the teaching process. After that, a Gaussian Mixture Regression (GMR) algorithm is applied to generate a synthesized trajectory to minimize position errors. Then a hybrid position/force controller is integrated to track the desired trajectory in the task space while considering the safety of human-robot interaction. The validation of our proposed method has been performed and proved by conducting massage tasks on a KUKA LBR iiwa robot platform. |
| first_indexed | 2024-12-13T05:55:49Z |
| format | Article |
| id | doaj.art-b026873e64254c59aa2208aed6ffaf3b |
| institution | Directory Open Access Journal |
| issn | 1662-5218 |
| language | English |
| last_indexed | 2024-12-13T05:55:49Z |
| publishDate | 2020-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj.art-b026873e64254c59aa2208aed6ffaf3b2022-12-21T23:57:26ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182020-06-011410.3389/fnbot.2020.00030544125An Enhanced Robot Massage System in Smart Homes Using Force Sensing and a Dynamic Movement PrimitiveChunxu Li0Chunxu Li1Ashraf Fahmy2Ashraf Fahmy3Shaoxiang Li4Johann Sienz5Centre for Robotics and Neural Systems, University of Plymouth, Plymouth, United KingdomShandong Marine Corrosion and Safety Protection Engineering Research Center, Qingdao University of Science and Technology, Qingdao, ChinaASTUTE 2020 in Future Manufacturing Research Institute, College of Engineering, Swansea University, Swansea, United KingdomDepartment of Electrical Power and Machines, Helwan University, Helwan, EgyptShandong Marine Corrosion and Safety Protection Engineering Research Center, Qingdao University of Science and Technology, Qingdao, ChinaASTUTE 2020 in Future Manufacturing Research Institute, College of Engineering, Swansea University, Swansea, United KingdomWith requirements to improve life quality, smart homes, and healthcare have gradually become a future lifestyle. In particular, service robots with human behavioral sensing for private or personal use in the home have attracted a lot of research attention thanks to their advantages in relieving high labor costs and the fatigue of human assistance. In this paper, a novel force-sensing- and robotic learning algorithm-based teaching interface for robot massaging has been proposed. For the teaching purposes, a human operator physically holds the end-effector of the robot to perform the demonstration. At this stage, the end position data are outputted and sent to be segmented via the Finite Difference (FD) method. A Dynamic Movement Primitive (DMP) is utilized to model and generalize the human-like movements. In order to learn from multiple demonstrations, Dynamic Time Warping (DTW) is used for the preprocessing of the data recorded on the robot platform, and a Gaussian Mixture Model (GMM) is employed for the evaluation of DMP to generate multiple patterns after the completion of the teaching process. After that, a Gaussian Mixture Regression (GMR) algorithm is applied to generate a synthesized trajectory to minimize position errors. Then a hybrid position/force controller is integrated to track the desired trajectory in the task space while considering the safety of human-robot interaction. The validation of our proposed method has been performed and proved by conducting massage tasks on a KUKA LBR iiwa robot platform.https://www.frontiersin.org/article/10.3389/fnbot.2020.00030/fullhybrid force/positionteaching by demonstrationdynamic motion primitivedynamic time warpinggaussian mixture regression |
| spellingShingle | Chunxu Li Chunxu Li Ashraf Fahmy Ashraf Fahmy Shaoxiang Li Johann Sienz An Enhanced Robot Massage System in Smart Homes Using Force Sensing and a Dynamic Movement Primitive Frontiers in Neurorobotics hybrid force/position teaching by demonstration dynamic motion primitive dynamic time warping gaussian mixture regression |
| title | An Enhanced Robot Massage System in Smart Homes Using Force Sensing and a Dynamic Movement Primitive |
| title_full | An Enhanced Robot Massage System in Smart Homes Using Force Sensing and a Dynamic Movement Primitive |
| title_fullStr | An Enhanced Robot Massage System in Smart Homes Using Force Sensing and a Dynamic Movement Primitive |
| title_full_unstemmed | An Enhanced Robot Massage System in Smart Homes Using Force Sensing and a Dynamic Movement Primitive |
| title_short | An Enhanced Robot Massage System in Smart Homes Using Force Sensing and a Dynamic Movement Primitive |
| title_sort | enhanced robot massage system in smart homes using force sensing and a dynamic movement primitive |
| topic | hybrid force/position teaching by demonstration dynamic motion primitive dynamic time warping gaussian mixture regression |
| url | https://www.frontiersin.org/article/10.3389/fnbot.2020.00030/full |
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