Human Motion Trajectory Prediction in Human-Robot Collaborative Tasks

Abstract

A method is introduced to predict human motion trajectory in the process of human-robot collaboration (HRC). In the method, the human-robot distances are assumed to be a Gaussian Process (GP). To achieve this, a human-robot handover task is conducted by a human and a collaborative robot, while the positions of the human hand and the robot end-effector are recorded. Some of the recorded data are used for the Gaussian Process Regression (GPR), a GP and a 95% confidence convince about the GP are obtained by the GPR. Experimental results show that about 80% of the testing data are included in the 95% confidence convince. The method and results here are useful to other human-robot collaborative tasks where existing human-robot relative motions, especially, the method is able to predict the human motion trajectory with varying initial position of the human hand and varying locations of the robot end-effector.