A comprehensive approach to the generation of human-like arm movements on robot NAO

Abstract

Humanoid robot is one of the most active frontiers in robotics. As the basic part of motion of humanoid robot, human-like motion planning is always one of the research hotspots and difficulties. In this paper, a comprehensive approach is proposed to help robot NAO generate the human-like arm movements. Firstly, a novel arm motion mode is built based on the Movement Primitives (MPs). The whole arm movement can be decoupled into different simple sub-movements with different motion models, which improves the accuracy and computational efficiency of human-like arm movements. Then a motion decision algorithm based on Bayesian Network (BN) is proposed to help robot NAO choose the suitable motion model among these MPs. Finally, according to the structure features of the MPs, the IK solutions can be classified into two categories: methods based on index and on geometrical constraints. Through the comprehensive approach, the robot NAO can generate various human-like arm movements with satisfactory accuracy. The availability of the approach is verified by similarity experiment and human-like movement experiment.