Model predictive control for constrained robot manipulator visual servoing tuned by reinforcement learning.

Journal: Mathematical biosciences and engineering : MBE
Published Date: Apr 10, 2023

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.

Authors

  • Jiashuai Li
    College of Intelligent Systems Science and Engineering, Harbin Engineering University, Nantong street, Harbin 150001, China.
  • Xiuyan Peng
    College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, Heilongjiang 150001, China.
  • Bing Li
  • Victor Sreeram
    School of Electrical, Electronic, and Computer Engineering, The University of Western Australia, Crawley, WA 6009, Australia.
  • Jiawei Wu
    Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, P. R. China.
  • Ziang Chen
    College of Intelligent Systems Science and Engineering, Harbin Engineering University, Nantong street, Harbin 150001, China.
  • Mingze Li
    College of Intelligent Systems Science and Engineering, Harbin Engineering University, Nantong street, Harbin 150001, China.

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