動力学的拘束を考慮したモデル予測制御のディープニューラルネットワーク学習

抄録

This paper proposes a trajectory planning method with deep neural network (DNN) which is trained by model predictive control (MPC) for dynamic manipulation. The novelty of this method is that trained DNN can receive target positions and environmental parameters to generate trajectories. The proposed method solves dynamic manipulation using dynamic constraint of the object with low calculation cost. This paper shows the effectiveness of the proposed method by demonstrations that a robot turns over pancakes under various parameters.