Autonomous ship technology, which includes real-time monitoring, satellite communication, and automatic navigation, is rapidly advancing. Despite significant research on single unmanned ships, there is a lack of studies on complex tasks, such as ship berthing using a swarm of autonomous tugboats. This review article provides an overview of various projects related to autonomous tugboats for ship berthing and discusses the research trends in the required technologies, including recognition, decision making, modeling, and control. We identify the areas that have been underexplored in existing studies and suggest future research directions to advance the field. Overall, this review contributes to a better understanding of the challenges and opportunities for the development of autonomous tugboats for ship berthing.

Surface-vehicle berthing is a complex and challenging task. It involves complicated ship dynamics owing to a large drift angle and the time-varying environmental disturbances induced by wind, wave, and current. Therefore, berthing requires state processing at each step and a controller that can respond to the non-linear behavior of the unmanned surface vehicle (USV). Herein, a systematic approach for the autonomous berthing of a USV is proposed. A state-machine approach is proposed to solve state transitions in the berthing step. A model reference adaptive controller was adopted to cope with the uncertainty of USV dynamics during berthing. Herein, the theoretical background and design of the adaptive controller are described. Simulation for the validation of the proposed method was conducted using the robot operating system Gazebo-based simulator. Finally, the respective performances of the proposed method and conventional controller were compared. Using the proposed berthing approach, an accurate and stable docking of small USVs became achievable.

With active research being conducted on maritime autonomous surface ships, it is becoming increasingly necessary to ensure the safety of unmanned surface vehicles (USVs). In this context, a key task is to correct their paths to avoid obstacles. This paper proposes a reactive collision avoidance algorithm to ensure the safety of USVs against obstacles. A global map is represented using a Gaussian mixture model, formulated using the expectation–maximization algorithm. Motion primitives are used to predict collision events and modify the USV’s trajectory. In addition, a controller for the target vessel is designed. Mapping is performed to demonstrate that the USV can implement the necessary avoidance maneuvers to prevent collisions with obstacles. The proposed method is validated by conducting collision avoidance simulations and autonomous navigation field tests with a small-scale autonomous surface vehicle (ASV) platform. Results indicate that the ASV can successfully avoid obstacles while following its trajectory.

When a policy trained with deep reinforcement learning (DRL) in simulation is deployed in the real world, its performance often deteriorates due to the Sim2Real gap. This study addresses this problem for Autonomous Surface Vessels (ASVs) by developing a robust collision-avoidance framework. We integrate a MATLAB-based ship dynamics model with ROS and Gazebo, and employ the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. To enhance robustness and generalization, we combine domain randomization and curriculum learning. As a result, the trained agent consistently achieved a high success rate of over 90% in unseen environments, significantly outperforming a baseline TD3 agent and a conventional PID controller. This demonstrates that the proposed Sim2Real methods are highly effective for creating robust control policies for ASVs. For future work, we plan to validate the learned policy through real-world experiments.

Automated docking technologies of marine boats have been enlightened by an increasing number of literature. This paper contributes to the literature by proposing a mathematical framework that automates "trailer loading" in the presence of wind disturbances, which is unexplored despite its importance to boat owners. The comprehensive pipeline of localization, system identification, and trajectory optimization is structured, followed by several techniques to improve performance reliability. The performance of the proposed method was demonstrated with a commercial pontoon boat in Michigan, in 2023, securing a success rate of 80\% in the presence of perception errors and wind disturbance. This result indicates the strong potential of the proposed pipeline, effectively accommodating the wind effect.