State estimation in control and systems engineering traditionally requires extensive manual system identification or data-collection effort. However, transformer-based foundation models in other domains have reduced data requirements by leveraging pre-trained generalist models. Ultimately, developing zero-shot foundation models of system dynamics could drastically reduce manual deployment effort. While recent work shows that transformer-based end-to-end approaches can achieve zero-shot performance on unseen systems, they are limited to sensor models seen during training. We introduce the foundation model unscented Kalman filter (FM-UKF), which combines a transformer-based model of system dynamics with analytically known sensor models via an UKF, enabling generalization across varying dynamics without retraining for new sensor configurations. We evaluate FM-UKF on a new benchmark of container ship models with complex dynamics, demonstrating a competitive accuracy, effort, and robustness trade-off compared to classical methods with approximate system knowledge and to an end-to-end approach. The benchmark and dataset are open sourced to further support future research in zero-shot state estimation via foundation models.

The deployment of autonomous navigation systems on ships necessitates accurate motion prediction models tailored to individual vessels. Traditional physics-based models, while grounded in hydrodynamic principles, often fail to account for ship-specific behaviors under real-world conditions. Conversely, purely data-driven models offer specificity but lack interpretability and robustness in edge cases. This study proposes a data-driven physics-based model that integrates physics-based equations with data-driven parameter optimization, leveraging the strengths of both approaches to ensure interpretability and adaptability. The model incorporates physics-based components such as 3-DoF dynamics, rudder, and propeller forces, while parameters such as resistance curve and rudder coefficients are optimized using synthetic data. By embedding domain knowledge into the parameter optimization process, the fitted model maintains physical consistency. Validation of the approach is realized with two container ships by comparing, both qualitatively and quantitatively, predictions against ground-truth trajectories. The results demonstrate significant improvements, in predictive accuracy and reliability, of the data-driven physics-based models over baseline physics-based models tuned with traditional marine engineering practices. The fitted models capture ship-specific behaviors in diverse conditions with their predictions being, 51.6% (ship A) and 57.8% (ship B) more accurate, 72.36% (ship A) and 89.67% (ship B) more consistent.

Reinforcement learning (RL) has shown promise in solving various combinatorial optimization problems. However, conventional RL faces challenges when dealing with real-world constraints, especially when action space feasibility is explicit and dependent on the corresponding state or trajectory. In this work, we focus on using RL in container shipping, often considered the cornerstone of global trade, by dealing with the critical challenge of master stowage planning. The main objective is to maximize cargo revenue and minimize operational costs while navigating demand uncertainty and various complex operational constraints, namely vessel capacity and stability, which must be dynamically updated along the vessel's voyage. To address this problem, we implement a deep reinforcement learning framework with feasibility projection to solve the master stowage planning problem (MPP) under demand uncertainty. The experimental results show that our architecture efficiently finds adaptive, feasible solutions for this multi-stage stochastic optimization problem, outperforming traditional mixed-integer programming and RL with feasibility regularization. Our AI-driven decision-support policy enables adaptive and feasible planning under uncertainty, optimizing operational efficiency and capacity utilization while contributing to sustainable and resilient global supply chains.

In this work, we propose a built-in Deep Learning Physics Optimization (DLPO) framework to set up a shape optimization study of the Duisburg Test Case (DTC) container vessel. We present two different applications: (1) sensitivity analysis to detect the most promising generic basis hull shapes, and (2) multi-objective optimization to quantify the trade-off between optimal hull forms. DLPO framework allows for the evaluation of design iterations automatically in an end-to-end manner. We achieved these results by coupling Extrality's Deep Learning Physics (DLP) model to a CAD engine and an optimizer. Our proposed DLP model is trained on full 3D volume data coming from RANS simulations, and it can provide accurate and high-quality 3D flow predictions in real-time, which makes it a good evaluator to perform optimization of new container vessel designs w.r.t the hydrodynamic efficiency. In particular, it is able to recover the forces acting on the vessel by integration on the hull surface with a mean relative error of 3.84\% \pm 2.179\% on the total resistance. Each iteration takes only 20 seconds, thus leading to a drastic saving of time and engineering efforts, while delivering valuable insight into the performance of the vessel, including RANS-like detailed flow information. We conclude that DLPO framework is a promising tool to accelerate the ship design process and lead to more efficient ships with better hydrodynamic performance.

Smartphones, like many consumer products, arrive in the US on giant container ships, vessels that are leading killers of endangered whales that play crucial roles in the climate and ocean health. Now a high-tech initiative called Whale Safe is detecting the huge marine mammals off the coast of San Francisco and alerting ship captains to slow down to avoid deadly collisions. Launched on Wednesday, Whale Safe aims to create "school zones" for imperiled blue whales, fin whales and humpback whales in busy shipping lanes, according to the project's managers at the Benioff Ocean Science Laboratory at the University of California at Santa Barbara and at the Bay Area's Marine Mammal Center. Speeders are caught by satellite surveillance and cited online. That gives consumers the opportunity to see, for instance, if that cruise they're contemplating is operated by a company with a history of ignoring sea speed limits.

Smartphones, like many consumer products, arrive in the US on giant container ships, vessels that are leading killers of endangered whales that play crucial roles in the climate and ocean health. Now a high-tech initiative called Whale Safe is detecting the huge marine mammals off the coast of San Francisco and alerting ship captains to slow down to avoid deadly collisions. Launched on Wednesday, Whale Safe aims to create "school zones" for imperilled blue whales, fin whales and humpback whales in busy shipping lanes, according to the project's managers at the Benioff Ocean Science Laboratory at the University of California at Santa Barbara and at the Bay Area's Marine Mammal Center. Speeders are caught by satellite surveillance and cited online. That gives consumers the opportunity to see, for instance, if that cruise they're contemplating is operated by a company with a history of ignoring sea speed limits.

The Mayflower Autonomous Ship finally arrived on the coast of Nova Scotia last month, marking the end of its long trek across the Atlantic. While the modern Mayflower is far from the first vessel to make that voyage, this small robotic boat is the largest to ever do so navigated by artificial intelligence with no humans aboard. A few technical hiccups notwithstanding, its trip is the latest evidence that the future of the high seas could be autonomous. Slowly, self-steering ships are becoming a reality. In Norway, an autonomous battery-powered container vessel is shuttling fertilizer between a factory and a local port, and pending a successful trial, it could be fully certified within the next two years.

Artificial intelligence could one day organize the world. As if in anticipation of this, a maritime platform developer called Orca AI has just begun a research trial of new safety systems for autonomous ships, equipping a vessel with artificial intelligence that recognizes other ships to safely guide it through busy sea traffic, according to a recent press release from the company. Orca AI was founded in 2018 by a pair of naval tech experts, and designs software platforms with extreme specificity for maritime vessels. The firm blends existing safety systems with sensors to enhance the navigation and safety of vessels making their way through crowded (and sometimes dangerous) waterways. Orca AI is headquartered in Israel, and aims to link sea-bound vessels with 24/7 land-based AI insights.

The cylindrical vessel sports a futuristic design like a surfaced submarine, it's sleek hull sculpted to slice through waves with ease. But step on board and things are out of the ordinary. The living quarters have been removed. Cars may dominate today's discussion about the future of autonomous transportation but some of the world's largest maritime companies are betting big on autonomous shipping. Within the next decade, driverless ships like the one just described could be hauling cargo around the world.

Gard's mission is: Together we enable sustainable maritime development. To deliver on this mission, we explore and support the development of emerging technologies including maritime autonomous surface ships. The Nordic countries are leading the way in this area and we are proud to be collaborating with Yara International (Yara) and their newly established company Yara Birkeland AS that is developing the well-known Norwegian autonomous logistics project, YARA BIRKELAND. Construction of the zero-emission autonomous containership has already begun. When the ship enters service in early 2020, she will be operated by onboard crew while the autonomous systems are being tested and certified safe.