Abstract This study is positioned within the framework of multi-ship collision avoidance. It presents algorithms developed for the automatic identification of risk-free areas and the manoeuvres required to ensure safe navigation in multi-ship encounters in high seas. Inputs for them are data received from an AIS system and data calculated for each target and some predefined safety parameters and the pertinent COLREGs to be applied. These algorithms emulate the sequence of assessments and decisions typically undertaken by the Officers in charge Of a Navigational Watch (OONWs) to navigate safely towards the next waypoint in multi-ship encounter scenarios. Using polar graphs for velocity vectors, and by assigning weights to individual cells within velocity areas, the developed algorithms allow for the automatic determination of the most appropriate manoeuvre, while ensuring compliance with COLREGs. The resulting output is suitable for integration into autonomous navigation systems.
This paper presents several considerations based on the grounding accident of the small Norwegian bulk carrier Kaami in Scotland 2020, a case which shows surprising deviations in the set-up, pre-voyage use, and the way the Electronic Chart Display and Information System (ECDIS) was subsequently used to monitor the voyage. Using Reason's Swiss cheese safety barrier perspective, we present and analyse eight safety barriers and how they were breached at the time of the accident. It was not possible to determine if these violations were done wilfully or because the navigators were unaware of the status of the chart system. Assuming the latter, we present design advice that we believe could have averted the accident. Using the Kaami case as an illustration, we advocate that the complexity of modern ECDIS possibly has grown to a point where it is evident that something must be done to address this.
Abstract One of the most important problems in maritime navigation is that of finding the ship’s position at sea. Nowadays, the ship’s position can be determine using Global Navigation Satellite Systems (GNSSs); however, celestial navigation methods are still taught and can be used as a second line method in case the GNSS fails. In this paper, we present a method proposed by Carl Gauss, in 1809, to determine the ship’s position at sea. The method proposed requires only the observation of two altitudes of the Sun at two different instants of the day and calculates the geographical position of the ship without the need of other information such as assumed position or the use of the Nautical Almanac. This self-contained method was implemented in an Excel spreadsheet and was tested using real observations of the Sun. The results obtained were compared with the GPS (Global Positioning System) coordinates and we concluded that the results are extremely satisfactory. The method proposed represents a good alternative when the electronic systems fail.
Safe navigation of maritime autonomous surface ships (MASS) relies on two capabilities: path planning and collision avoidance. This review surveys classical algorithms and modern AI techniques for embedding the International Regulations for Preventing Collisions at Sea (COLREGs) into autonomous navigation. We organise prior work into three families—classical search/optimisation, real-time reactive methods, and learning-based approaches—and discuss their strengths and limitations with respect to rules compliance, computational cost, and onboard constraints. Building on these insights, we outline a large-language-model framework, Navigation-GPT, which couples reasoning-and-acting (ReAct) prompting with low-rank adaptation (LoRA). We further propose a three-phase deployment roadmap for MASS: core model integration, domain fine-tuning, and integrated operations. The paper concludes with open challenges and research directions toward reliable, explainable, and fully compliant MASS navigation.
