Data Driven Control for marine vehicle maneuvering

Alam, M. S.

(2023)

ABSTRACT The majority of global marine accidents are caused by human decision-making errors, which has resulted in increased interest in automation within the marine industry. However, obstacle avoidance for autonomous surface vehicles in unknown environments is particularly difficult. This study investigates the possibility of utilizing a deep reinforcement learning (DRL) approach to control an underactuated autonomous surface vehicle following a predetermined path while avoiding collisions with static and dynamic obstacles. The ship’s movement is modelled using a three-degree-of-freedom (3-DOF) dynamic model, with the KRISO container ship (KCS) being selected for the study due to its extensive use in previous research and readily available hydrodynamic coefficients for numerical modelling. The study evaluates the performance of various DRL algorithms, such as Deep Q-Network (DQN), Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO) algorithms, for path following and their effectiveness in the presence of wind, as well as comparing them to the traditional PD controller. The study also explores DQN and DDPG algorithms for both static and dynamic obstacle avoidance and proposes a hybrid network that uses two networks for improved path following and obstacle avoidance capabilities.