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.
DOI
10.3389/fnbot.2020.00030
Publication Date
2020-06-29
Publication Title
Frontiers in Neurorobotics
Publisher
Frontiers Media
ISSN
1662-5218
Embargo Period
2024-11-22
Recommended Citation
Li, C., Fahmy, A., Li, S., & Sienz, J. (2020) 'An Enhanced Robot Massage System in Smart Homes Using Force Sensing and Dynamic Movement Primitive', Frontiers in Neurorobotics, . Frontiers Media: Available at: https://doi.org/10.3389/fnbot.2020.00030
