President Donald Trump is turning his attention to the U.S. shipbuilding industry, which is leagues behind its near-peer competitor China, and recently signed an executive order designed to reinvigorate it. Trump's April 10 order instructs agencies to develop a Maritime Action Plan and orders the U.S. trade representative to compile a list of recommendations to address China's "anticompetitive actions within the shipbuilding industry," among other things. Additionally, the executive order instructs a series of assessments regarding how the government could bolster financial support through the Defense Production Act, the Department of Defense Office of Strategic Capital, a new Maritime Security Trust Fund, investment from shipbuilders from allied countries and other grant programs. But simply throwing money at the shipbuilding industry won't solve the problem, according to Bryan Clark, director of the Hudson Institute think tank's Center for Defense Concepts and Technology. "It is unlikely that just putting more money into U.S. shipbuilding – even with foreign technical assistance – will make U.S. commercial shipbuilders competitive with experienced and highly-subsidized shipyards in China, Korea, or Japan," Clark said in a Monday email to Fox News Digital.

That began to change with the partnership between the shipyard and the university. In the '90s, that relationship got a big boost when the foundation behind the Mærsk shipping company funded the creation of the Mærsk Mc-Kinney Møller Institute (MMMI), a center dedicated to studying autonomous systems. The Lindø shipyard eventually wound down its robotics program, but research continued at the MMMI. Students flocked to the institute to study robotics. And it was there that three researchers had the idea for a more lightweight, flexible, and easy-to-use industrial robot arm. That idea would become a startup called Universal Robots, Odense's first big robotics success story.

An onboard computer watching your every move might sound like something out of 2001: A Space Odyssey. But now, a futuristic superyacht plans to use AI to learn what you want before you even realise it. Just like HAL 9000 from Stanley Kubrick's sci-fi classic, the ship's computer will spy on its passengers to learn more about their desires. Designed by the Italian shipyard Rossinavi, the 43-metre-long Solsea will use that information to tailor itself to the needs of individual guests. Rossinavi says that this onboard AI has been designed to make travel more comfortable and maximise the yacht's eco-friendly potential.

Ice conditions often require ships to reduce speed and deviate from their main course to avoid damage to the ship. In addition, broken ice fields are becoming the dominant ice conditions encountered in the Arctic, where the effects of collisions with ice are highly dependent on where contact occurs and on the particular features of the ice floes. In this paper, we present AUTO-IceNav, a framework for the autonomous navigation of ships operating in ice floe fields. Trajectories are computed in a receding-horizon manner, where we frequently replan given updated ice field data. During a planning step, we assume a nominal speed that is safe with respect to the current ice conditions, and compute a reference path. We formulate a novel cost function that minimizes the kinetic energy loss of the ship from ship-ice collisions and incorporate this cost as part of our lattice-based path planner. The solution computed by the lattice planning stage is then used as an initial guess in our proposed optimization-based improvement step, producing a locally optimal path. Extensive experiments were conducted both in simulation and in a physical testbed to validate our approach.

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.

Ship design is a complex design process that may take a team of naval architects many years to complete. Improving the ship design process can lead to significant cost savings, while still delivering high-quality designs to customers. A new technology for ship hull design is diffusion models, a type of generative artificial intelligence. Prior work with diffusion models for ship hull design created high-quality ship hulls with reduced drag and larger displaced volumes. However, the work could not generate hulls that meet specific design constraints. This paper proposes a conditional diffusion model that generates hull designs given specific constraints, such as the desired principal dimensions of the hull. In addition, this diffusion model leverages the gradients from a total resistance regression model to create low-resistance designs. Five design test cases compared the diffusion model to a design optimization algorithm to create hull designs with low resistance. In all five test cases, the diffusion model was shown to create diverse designs with a total resistance less than the optimized hull, having resistance reductions over 25%. The diffusion model also generated these designs without retraining. This work can significantly reduce the design cycle time of ships by creating high-quality hulls that meet user requirements with a data-driven approach.

The thesis develops a view of design in a concept formation framework and outlines a language to describe both the object of the design and the process of designing. The unknown object at the outset of the design work may be seen as an unknown concept that the designer is to define. Throughout the process, she develops a description of this object by relating it to known concepts. The search stops when the designer is satisfied that the design specification is complete enough to satisfy the requirements from it once built. It is then a collection of propositions that all contribute towards defining the design object - a collection of sentences describing relationships between the object and known concepts. Also, the design process itself may be described by relating known concepts - by organizing known abilities into particular patterns of activation, or mobilization. In view of the demands posed to a language to use in this concept formation process, the framework of a Design Process Language (DPL) is developed. The basis for the language are linguistic categories that act as classes of relations used to combine concepts, containing relations used for describing process and object within the same general system, with some relations being process specific, others being object specific, and with the bulk being used both for process and object description. Another outcome is the distinction of modal relations, or relations describing futurity, possibility, willingness, hypothetical events, and the like. The design process almost always includes aspects such as these, and it is thus necessary for a language facilitating design process description to support such relationships to be constructed. The DPL is argued to be a foundation whereupon to build a language that can be used for enabling computers to be more useful - act more intelligently - in the design process.

Polar codes, developed on the foundation of Arikan's polarization kernel, represent a breakthrough in coding theory and have emerged as the state-of-the-art error-correction-code in short-to-medium block length regimes. Importantly, recent research has indicated that the reliability of polar codes can be further enhanced by substituting Arikan's kernel with a larger one, leading to a faster polarization. However, for short-to-medium block length regimes, the development of polar codes that effectively employ large kernel sizes has not yet been realized. In this paper, we explore a novel, non-linear generalization of polar codes with an expanded kernel size, which we call DeepPolar codes. Our results show that DeepPolar codes effectively utilize the benefits of larger kernel size, resulting in enhanced reliability compared to both the existing neural codes and conventional polar codes.

Tailoring polar code construction for decoding algorithms beyond successive cancellation has remained a topic of significant interest in the field. However, despite the inherent nested structure of polar codes, the use of sequence models in polar code construction is understudied. In this work, we propose using a sequence modeling framework to iteratively construct a polar code for any given length and rate under various channel conditions. Simulations show that polar codes designed via sequential modeling using transformers outperform both 5G-NR sequence and Density Evolution based approaches for both AWGN and Rayleigh fading channels.

In this paper, a fine-tuning method of the parameters in the MMG model for the real-scale ship is proposed. In the proposed method, all of the arbitrarily indicated target parameters of the MMG model are tuned simultaneously in the framework of SI using time series data of real-sale ship maneuvering motion data to steadily improve the accuracy of the MMG model. Parameter tuning is formulated as a minimization problem of the deviation of the maneuvering motion simulated with given parameters and the real-scale ship trials, and the global solution is explored using CMA-ES. By constraining the exploration ranges to the neighborhood of the previously determined parameter values, the proposed method limits the output in a realistic range. The proposed method is applied to the tuning of 12 parameters for a container ship with five different widths of the exploration range. The results show that, in all cases, the accuracy of the maneuvering simulation is improved by applying the tuned parameters to the MMG model, and the validity of the proposed parameter fine-tuning method is confirmed.