Research on path tracking control method of unmanned surface vehicle based on deep reinforcement learning

Rui Guo; Wei Yuan

doi:10.1117/12.2606571

28 October 2021 Research on path tracking control method of unmanned surface vehicle based on deep reinforcement learning

Rui Guo, Wei Yuan

Author Affiliations +

Proceedings Volume 11884, International Symposium on Artificial Intelligence and Robotics 2021; 118841P (2021) https://doi.org/10.1117/12.2606571
Event: International Symposium on Artificial Intelligence and Robotics 2021, 2021, Fukuoka, Japan

Abstract

Due to the nonlinear and underactuated characteristics of unmanned surface vehicle system and the uncertainty of environmental model, it is hard to establish accurate dynamic model and control law obtained by traditional algorithm which is too complex and has no engineering practice realization. In this paper, based on deep reinforcement learning algorithm of deep deterministic policy gradients, the line of sight algorithm is used firstly to obtains the expected value of heading angle of USV according to the current time position and the expected trajectory of USV. Meanwhile, we adopt the double Gaussian reward function to evaluate the training action, so as to obtain the optimal control action to realize the accurate tracking control. Finally, compared with explicit model predictive controller and linear quadratic regulator, the designed track controller based on DDPG has shorter adjusting time and smaller overshoot than explicit model predictive controller and linear quadratic regulator.

Citation Download Citation

Rui Guo and Wei Yuan "Research on path tracking control method of unmanned surface vehicle based on deep reinforcement learning", Proc. SPIE 11884, International Symposium on Artificial Intelligence and Robotics 2021, 118841P (28 October 2021); https://doi.org/10.1117/12.2606571

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