Flocking of Under-Actuated Unmanned Surface Vehicles via Deep Reinforcement Learning and Model Predictive Path Integral Control
Chao Pan, Zhouhua Peng, Yongming Li, Bing Han, Dan Wang
Abstract
This article addresses flocking control of a swarm of under-actuated unmanned surface vehicles (USVs) subject to stationary and moving obstacles guided by a dynamic leader. The leader and follower USVs suffer from fully unknown models. A fully data-driven learning and control method is proposed to achieve a stable flocking behavior via deep reinforcement learning (DRL) and model predictive path integral (MPPI) control. Specifically, by leveraging the recorded input and output data, a deep neural network is first trained for approximating the dynamics model of each USV. Next, based on the learned vehicle dynamics, MPPI control laws are designed such that flocking and collision avoidance tasks can be simultaneously achieved. It is indicated that after learning by utilizing random and new data, stable flocking can be achieved without using any model information. Simulation results validate the effectiveness of the proposed fully data-driven learning and control method for flocking of under-actuated USVs.