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An Enhanced Robot Massage System in Smart Homes Using Force Sensing and a Dynamic Movement Primitive / Chunxu Li; Ashraf Fahmy; Shaoxiang Li; Johann Sienz

Frontiers in Neurorobotics, Volume: 14

Swansea University Author: Johann, Sienz

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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...

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Published in: Frontiers in Neurorobotics
ISSN: 1662-5218
Published: Frontiers Media SA 2020
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa54859
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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.
Keywords: hybrid force/position, teaching by demonstration, dynamic motion primitive, dynamic time warping, gaussian mixture regression
College: College of Engineering