MMJM: A High-Precision Motion Model for Human Arm Reaching Motion

Abstract

In human-robot interaction, it is essential for robots to accurately model and predict the movements of their human partners, allowing them to adjust their behavior in advance to avoid conflicts with humans and to improve the efficiency and safety of the interaction process. This paper aims to develop a highly accurate motion model to model and predict the motion trajectory of the human hand. Firstly, the causes and characteristics of the errors generated by the existing minimum jerk model (MJM) in modeling the trajectories of real human arm reaching movements are investigated, and a new motion model called MMJM is proposed based on a 2nd order Fourier modification term. Secondly, how to use the proposed MMJM to predict the human arm reaching motion is studied. Finally, the effectiveness of the proposed MMJM is validated by the human arm reaching movement experiments, which shows that the MMJM has better modeling accuracy and prediction accuracy than the existing MJM.