Autonomous docking remains one of the most challenging maneuvers in marine robotics, requiring precise control and robust perception in confined spaces. This paper presents a novel approach integrating Model Predictive Path Integral(MPPI) control with real-time LiDAR-based dock detection for autonomous surface vessel docking. Our framework uniquely combines probabilistic trajectory optimization with a multiobjective cost function that simultaneously considers docking precision, safety constraints, and motion efficiency. The MPPI controller generates optimal trajectories by intelligently sampling control sequences and evaluating their costs based on dynamic clearance requirements, orientation alignment, and target position objectives. We introduce an adaptive dock detection pipeline that processes LiDAR point clouds to extract critical geometric features, enabling real-time updates of docking parameters. The proposed method is extensively validated in a physics-based simulation environment that incorporates realistic sensor noise, vessel dynamics, and environmental constraints. Results demonstrate successful docking from various initial positions while maintaining safe clearances and smooth motion characteristics.

Autonomous surface vessels (ASVs) are becoming increasingly significant in enhancing the safety and sustainability of maritime operations. To ensure the reliability of modern control algorithms utilized in these vessels, digital twins (DTs) provide a robust framework for conducting safe and effective simulations within a virtual environment. Digital twins are generally classified on a scale from 0 to 5, with each level representing a progression in complexity and functionality: Level 0 (Standalone) employs offline modeling techniques; Level 1 (Descriptive) integrates sensors and online modeling to enhance situational awareness; Level 2 (Diagnostic) focuses on condition monitoring and cybersecurity; Level 3 (Predictive) incorporates predictive analytics; Level 4 (Prescriptive) embeds decision-support systems; and Level 5 (Autonomous) enables advanced functionalities such as collision avoidance and path following. These digital representations not only provide insights into the vessel's current state and operational efficiency but also predict future scenarios and assess life endurance. By continuously updating with real-time sensor data, the digital twin effectively corrects modeling errors and enhances decision-making processes. Since DTs are key enablers for complex autonomous systems, this paper introduces a comprehensive methodology for establishing a digital twin framework specifically tailored for ASVs. Through a detailed literature survey, we explore existing state-of-the-art enablers across the defined levels, offering valuable recommendations for future research and development in this rapidly evolving field.

Maritime autonomous transportation has played a crucial role in the globalization of the world economy. Deep Reinforcement Learning (DRL) has been applied to automatic path planning to simulate vessel collision avoidance situations in open seas. End-to-end approaches that learn complex mappings directly from the input have poor generalization to reach the targets in different environments. In this work, we present a new strategy called state-action rotation to improve agent's performance in unseen situations by rotating the obtained experience (state-action-state) and preserving them in the replay buffer. We designed our model based on Deep Deterministic Policy Gradient, local view maker, and planner. Our agent uses two deep Convolutional Neural Networks to estimate the policy and action-value functions. The proposed model was exhaustively trained and tested in maritime scenarios with real maps from cities such as Montreal and Halifax. Experimental results show that the state-action rotation on top of the CVN consistently improves the rate of arrival to a destination (RATD) by up 11.96% with respect to the Vessel Navigator with Planner and Local View (VNPLV), as well as it achieves superior performance in unseen mappings by up 30.82%. Our proposed approach exhibits advantages in terms of robustness when tested in a new environment, supporting the idea that generalization can be achieved by using state-action rotation.

There has been a lot of discussion about autonomous vehicles on the land and in the air, but what about on the sea? While the world got the first glimpse of a fully autonomous ferry thanks to the collaboration between Rolls-Royce and Finferries, the state-owned ferry operator of Finland, there's still quite a bit of work to be done before we can expect the world's waterways to be overtaken with autonomous vessels. Even though we might be years or even decades away from the majority of vessels becoming autonomous, there are certainly artificial intelligence algorithms at work today. A fully autonomous ship would be considered a vessel that can operate on its own without a crew. Remote ships are those that are operated by a human from shore, and an automated ship runs software that manages its movements. As the technology matures, more types of ships will likely transition from being manned to having some autonomous capabilities.

The Defense Department christened the Sea Hunter, a 132-foot robot ghost ship designed to seek out and track diesel-powered submarines across the ocean. The start of the test phase for the program on Thursday signals a new dawn for autonomous systems at sea, which, Pentagon officials say, will perform an ever-wider variety of jobs and could fundamentally change the way militaries operate on the water. The Sea Hunter is the first of a new type of ocean drone, called an Anti-submarine Warfare Continuous Trail Unmanned Vessel, or ACTUV. The goal of the program: field an autonomous ship with the range and endurance to go anywhere in the world while avoiding collisions with other ships and obeying the rules of navigation. "Current unmanned surface vessel systems and concepts are operated as close-adjuncts to conventional manned ships – they are launched and recovered from manned ships, tele-operated from manned ships, and are limited to direct support of manned ship missions. The ACTUVsystem will be a first of its kind unmanned naval vessel that is designed and sized for theater or global independent deployment," reads the program's description from 2014.

The autonomous vessel can travel on the high seas at speeds up to 27 knots for months on end without a single crew member. The 130-foot ACTUV can be remote-controlled, but its primary use is as an autonomous vessel that can operate safely near manned ships and accommodate all weather conditions. No crew means greater safety for potentially dangerous missions like countermining and submarine tracking. ACTUV is now slated for joint testing in open water between DARPA and the Office of Naval Research to determine viable payloads and potential uses for this sub-hunter robot.