Human-robot cooperative manipulation with motion estimation using minimum-jerk model

In this paper, a control method of robots for human-robot cooperative manipulation is investigated. We propose estimating human motion using the minimum jerk model for smooth cooperation. Using nonlinear least-squares method, we identify two parameters of the minimum-jerk model in real-time. The estimated position of the human hand is used to determine the desired position of the end-effector of the manipulator in virtual compliance control. The motion estimation enables robots to coordinate actively even for unknown trajectories of handled objects that human partners intend. We implemented the proposed method on an industrial 6-degree-of-freedom manipulator with a force sensor. In experiments of cooperative manipulation of a rubber pipe, the motion estimation improved human feeling in coordination. The improvement was quantitatively evaluated from the viewpoint of unnecessary energy transfer.