Model predictive control for constrained robot manipulator visual servoing tuned by reinforcement learning
Jiashuai Li, Xiuyan Peng, Bing Li, Victor Sreeram, Jiawei Wu, Ziang Chen, Mingze Li
Abstract
For constrained image-based visual servoing (IBVS) of robot manipulators, a model predictive control (MPC) strategy tuned by reinforcement learning (RL) is proposed in this study. First, model predictive control is used to transform the image-based visual servo task into a nonlinear optimization problem while taking system constraints into consideration. In the design of the model predictive controller, a depth-independent visual servo model is presented as the predictive model. Next, a suitable model predictive control objective function weight matrix is trained and obtained by a deep-deterministic-policy-gradient-based (DDPG) RL algorithm. Then, the proposed controller gives the sequential joint signals, so that the robot manipulator can respond to the desired state quickly. Finally, appropriate comparative simulation experiments are developed to illustrate the efficacy and stability of the suggested strategy.