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An Enhanced Robot Massage System in Smart Homes Using Force Sensing and a Dynamic Movement Primitive
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Ashraf Fahmy Abdo ,
Shaoxiang Li,
Johann Sienz
Frontiers in Neurorobotics, Volume: 14
Swansea University Authors:
Chunxu Li , Ashraf Fahmy Abdo , Johann Sienz
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DOI (Published version): 10.3389/fnbot.2020.00030
Abstract
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...
| Published in: | Frontiers in Neurorobotics |
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| ISSN: | 1662-5218 |
| Published: |
Frontiers Media SA
2020
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa54859 |
| Abstract: |
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. |
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| Keywords: |
hybrid force/position, teaching by demonstration, dynamic motion primitive, dynamic time warping, gaussian mixture regression |
| College: |
Faculty of Science and Engineering |