Knowledge

Offshore pipelines and structures require regular marine growth removal and inspection to ensure structural integrity. These operations are typically carried out by Remotely Operated Vehicles (ROVs) and demand reliable and accurate feedback signals for operating the ROVs efficiently under harsh offshore conditions. This study investigates and quantifies how sensor delays impact the expected control performance without the need for defining the control parameters. Input-output (IO) controllability analysis of the open-loop system is applied to find the lower bound of the H-infinity peaks of the unspecified optimal closed-loop systems. The performance analyses have shown that near-structure operations, such as pipeline inspection or cleaning, in which small error tolerances are required, have a small threshold for the time delays. The IO controllability analysis indicates that off-structure navigation allow substantial larger time delays. Especially heading is vulnerable to time delay; however, fast-responding sensors usually measure this motion. Lastly, a sensor comparison is presented where available sensors are evaluated for each ROV motion’s respective sensor-induced time delays. It is concluded that even though off-structure navigation have larger time delay tolerance the corresponding sensors also introduce substantially larger time delays.

The European maritime transport policy recognizes the importance of the waterborne transport systems as key elements for sustainable growth in Europe. A major goal is to transfer more than 50% of road transport to rail or waterways within 2050. However, waterways are at a disadvantage as they normally depend on transhipment and land transport to and from final destination. To meet this challenge we need a completely new approach to short sea and inland waterways shipping in Europe. This needs to include ships as well as ports and the digital information exchanges between them. A key element in this is automation of ships, ports and administrative tasks. The AEGIS project has been funded by the EU Commission to develop new knowledge and technology to address this challenge.

The oceans, covering approximately 70% of Earth's surface, play a pivotal role in climate regulation, biodiversity, and biogeochemical processes. The large and growing volume and complexity of ocean data, spanning diverse disciplines and formats, and dispersed across a wide range of sources, presents opportunities and challenges for advancing scientific research, informing policy, and addressing societal needs.

In this review paper we aim to create an easy-to-navigate map of the field of ocean data, enabling the reader to establish a broad understanding of the ocean data sector, and bridging gaps between different disciplines and levels of familiarity with ocean data. This is done through the concept of the "data ecosystem", which is used to describe the actors, organisations, and infrastructures involved in all aspects of the data value chain. We propose a structured ocean data ecosystem model as a method for comprehensive mapping of the ocean data market landscape. The proposed model consists of five key elements: stakeholders, societal elements, data sources and product offering, standards and best practices, and emerging technologies. We provide an up-to-date analysis of ocean data sources and emerging solutions and a summary of relevant data standardization efforts such as marine standards, vocabularies, and ontologies. All this will promote the development of needs-based solutions, components, products, services, and technologies, thus contributing to the evolution of the ocean data ecosystem and promoting data-based ocean research.

Unmanned autonomous cargo ships may change the maritime industry, but there are issues regarding reliability and maintenance of machinery equipment that are yet to be solved. This article examines the applicability of the Reliability Centred Maintenance (RCM) method for assessing maintenance needs and reliability issues on unmanned cargo ships. The analysis shows that the RCM method is generally applicable to the examination of reliability and maintenance issues on unmanned ships, but there are also important limitations. The RCM method lacks a systematic process for evaluating the effects of preventive versus corrective maintenance measures. The method also lacks a procedure to ensure that the effect of the length of the unmanned voyage in the development of potential failures in machinery systems is included. Amendments to the RCM method are proposed to address these limitations, and the amended method is used to analyse a machinery system for two operational situations: one where the vessel is conventionally manned and one where it is unmanned. There are minor differences in the probability of failures between manned and unmanned operation, but the major challenge relating to risk and reliability of unmanned cargo ships is the severely restricted possibilities for performing corrective maintenance actions at sea.

Highly reliable situation awareness is a main driver to enhance safety via autonomous technology in the marine industry. Groundings, ship collisions and collisions with bridges illustrate the need for enhanced safety. Authority for a computer to suggest actions or to take command, would be able to avoid some accidents where human misjudgement was a core reason. Autonomous situation awareness need be conducted with extreme confidence to let a computer algorithm take command. The anticipation of how a situation can develop is by far the most difficult step in situation awareness, and anticipation is the subject of this article. The IMO International Regulations for Preventing Collisions
at Sea (COLREGS), describe the regulatory behaviours of marine vessels relative to each other, and correct interpretation of situations is instrumental to safe navigation. Based on a breakdown of COLREGS rules, this article presents a framework to represent manoeuvring behaviours that are expected when all vessels obey the rules. The article shows how nested finite automata can segregate situation assessment from decision making and provide a testable and repeatable algorithm. The suggested method makes it possible to anticipate own ship and other vessels’ manoeuvring in a multi-vessel scenario. The framework is validated using scenarios from a full-mission simulator.

Highly reliable situation awareness is a main driver to enhance safety via autonomous technology in the marine industry. Groundings, ship collisions and collisions with bridges illustrate the need for enhanced safety. Authority for a computer to suggest actions or to take command, would be able to avoid some accidents where human misjudgement was a core reason. Autonomous situation awareness need be conducted with extreme confidence to let a computer algorithm take command. The anticipation of how a situation can develop is by far the most difficult step in situation awareness, and anticipation is the subject of this article. The IMO International Regulations for Preventing Collisions
at Sea (COLREGS), describe the regulatory behaviours of marine vessels relative to each other, and correct interpretation of situations is instrumental to safe navigation. Based on a breakdown of COLREGS rules, this article presents a framework to represent manoeuvring behaviours that are expected when all vessels obey the rules. The article shows how nested finite automata can segregate situation assessment from decision making and provide a testable and repeatable algorithm. The suggested method makes it possible to anticipate own ship and other vessels’ manoeuvring in a multi-vessel scenario. The framework is validated using scenarios from a full-mission simulator.

This paper introduces a resilience assessment methodology for sustainable autonomous maritime transport networks developed by the European project entitled “Advanced, Efficient, and Green Intermodal Systems” (AEGIS). This problem being addressed in this paper concerns the investigation of threats, incidents, and risks in an autonomous- and sustainable shipping context, and the research question is the development of both preventive measures and reactive actions to maintain an acceptable level of operational constraints. The paper's methodology aids in designing sustainable logistics systems for highly automated waterborne transport, identifying threats and barriers to mitigate event consequences, thereby facilitating a seamless green transition. To examine the usability, this methodology is applied in a case study for cargo transportation, where we in this paper consider the maritime corridor between Trondheim and Rotterdam. The findings encompass the spectrum of possible actions to prevent and mitigate unwanted events and enhance resilience and flexibility. This can be used as a tool to respond to unwanted threats, enhance safety, and introduce new strategies. These results are deemed important as resilience is one of the prerequisites for the development of a sustainable transport system. This is true both for the companies that are engaged in the operation of such systems and for policymakers.

The purpose of this project is therefore to develop a software tool that can implement an automated intelligent registration (artificial intelligence) of the catch of cod on board the vessel. The project can both support the ongoing camera projects, but also functions as a forward-looking method where the concept of this approach is that the camera focuses on the catch and can be implemented without human supervision. This has a number of potential advantages, including that human supervision is avoided, the number of cameras can probably be reduced to just one (although possibly a stereo camera), labor resources are saved by automated monitoring, it will be possible to reduce the amount of data, fishermen can target selective fishing based on the information obtained, increased precision in relation to possible legal
use of the observations and overall it will reduce costs. The project supports the monitoring that has been initiated in the Kattegat, but should also be seen as a future development, including internationally, where the focus is on building monitoring/surveillance around the use of images as documentation of the catch. An extremely important element of the project is to create a high-quality dataset that can be used internationally to improve algorithms and intensify research.

Given the move toward automation, an increased focus on the liability for technical defects must be anticipated. This brings into play liability regimes that have traditionally been less used in the maritime area. One of these liability regimes is product liability. It is the purpose of this contribution to examine the implications of product liability rules in the maritime area, seen in light of the automation of ships.

Autonomous ships have been a hot topic in maritime transport research in the past years. However, there are still many unanswered questions regarding what defines an autonomous ship and the potential and limitations of implementing and operating these. In this video, Stig Eriksen from SDU/SIMAC explore these topics.

The video is developed in collaboration with MARLOG.