Deep Reinforcement Learning Based Tracking Control of an Autonomous Surface Vessel in Natural Waters

TL;DR

This paper presents a Deep Reinforcement Learning approach for controlling autonomous surface vessels, demonstrating superior tracking accuracy and disturbance rejection compared to traditional model predictive control in both simulations and real-world environments.

Contribution

The paper introduces a DRL-based control method for ASV trajectory tracking that outperforms NMPC, especially under environmental disturbances and measurement noise.

Findings

DRL controller reduces tracking error by 53.33% in simulations.

DRL controller reduces tracking error by 35.51% in real water environments.

DRL offers better disturbance rejection than NMPC in natural waters.

Abstract

Accurate control of autonomous marine robots still poses challenges due to the complex dynamics of the environment. In this paper, we propose a Deep Reinforcement Learning (DRL) approach to train a controller for autonomous surface vessel (ASV) trajectory tracking and compare its performance with an advanced nonlinear model predictive controller (NMPC) in real environments. Taking into account environmental disturbances (e.g., wind, waves, and currents), noisy measurements, and non-ideal actuators presented in the physical ASV, several effective reward functions for DRL tracking control policies are carefully designed. The control policies were trained in a simulation environment with diverse tracking trajectories and disturbances. The performance of the DRL controller has been verified and compared with the NMPC in both simulations with model-based environmental disturbances and in natural waters. Simulations show that the DRL controller has 53.33% lower tracking error than that of NMPC. Experimental results further show that, compared to NMPC, the DRL controller has 35.51% lower tracking error, indicating that DRL controllers offer better disturbance rejection in river environments than NMPC.