Human-Robot Collaborative Manipulation with the Suppression of Human-caused Disturbance

A method for robot control is proposed in this paper to suppress human-caused disturbance in human-robot collaborative manipulation. In the method, the robot control algorithms are chosen according to the impacts of human motion on the manipulated objects: the modified model predictive controller is used when the impact is large, and the impedance controller is used when the impact is small. In the modified model predictive control, the human motion in a specific direction is considered as disturbance, the disturbance is observed, predicted, and its impact on the manipulated objects’ stability is estimated. The robot control parameters are then optimized based on the estimation. A series of simulation and physical experiments are conducted. The results show that the modified model predictive control shows better stability than the impedance control and model predictive control. Specifically, the maximum displacement of the manipulated objects decreases by 70% compared with the impedance control and 44% compared with the model predictive control.