Human-robot skill transfer systems for mobile robot based on multi sensor fusion

Abstract

Teaching by demonstration (TbD) is a powerful way to generalize the skills learned from human demonstrations to fulfill complex requirements. The mobile robot can learn new skills from interaction with human being in an intuitive way. In this paper, we propose a human-robot skill transfer system for a mobile robot that is instructed to follow a trajectory demonstrated by a human teacher wearing a motion capturing device while the Kinect sensor is recording the trajectory. With multi-modal sensor fusion, the position and velocity of the human teacher are enhanced for the correction and accuracy. A nonlinear system named a dynamic movement primitive (DMP) is modeled by the trajectories data. The Gaussian mixture model is applied for the appraisal of DMP, so as to model numerous trajectories through the teaching of a demonstration. Further, to achieve the accuracy of the trajectory tracking, a novel nonlinear model predictive control (MPC) approach is proposed for motion control. By comparison with other obstacle avoidance works, the results show improved performances in terms of the computing time and length of the trajectory.