An Enhanced Robot Massage System in Smart Homes Using Force Sensing and a Dynamic Movement Primitive - Li, Chunxu; Fahmy, Ashraf; Li, Shaoxiang; Sienz, Johann (Frontiers in Neurorobotics, 2020)
With the requirements for improving life quality, smart home and healthcare have gradually become a future lifestyle. In particular, the service robots with human behavioural sensing for private or personal use in the home have attracted a lot of research attention thanks to their advantages in releasing the 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. 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 Gaussian Mixture Model (GMM) is employed for the evaluation of DMP to generate multiple patterns after the completion of the teaching process. After that, 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 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.