人間・ロボット協調系のためのベイズ最適化を用いたダンピング場パラメータの学習

抄録

In impedance control, impedance parameters can be adjusted to improve the performance of human-robot collaboration. This paper proposes a new method to adapt the damping parameter based on the potential field method widely used in path planning. First, a damping field is generated to modify the damping value based on the current end-effector position. Bayesian optimization is then used to learn the parameters of the constructed damping field. Finally, experiments are conducted with a 2-DOF planar robot arm in a point-to-point collaborative motion, and results show that the proposed method help improve the performance of tasks that require high accuracy.