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.
Autonomous unmanned underwater vehicles (UUVs) play a vital role in diverse underwater operations; localization is of great interest for UUVs mirroring the trend seen in self-driving surface and aerial vehicles. Unlike their land and aerial counterparts, underwater environments lack reliable Global Navigation Satellite Systems (GNSS) due to radio wave attenuation in water. Hence, alternative localization methods are imperative for both navigation and operational purposes. This study thoroughly reviews sensor technologies for underwater localization, including sonar, Doppler velocity log, cameras, and more. Different operations necessitate distinct localization accuracies and vehicle and sensor choices. Environmental factors, such as turbidity, waves, and sound disturbances, impact sensor performance. Conclusions are given on the coincidence between operational requirements and sensor specifications, with special attention to the open concerns. These considerations include aspects such as the line of sight for acoustic positioning systems and the requirement for a feature-rich environment for visual sensors. Lastly, a prediction for the future of underwater localization is given, where the tendencies indicate lower costs for sensors, making operation-specific vehicles more attractive, which aligns with an increased demand for cost-efficient autonomous offshore operations.
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.
Marine autonomy research has focused on algorithmic and technical developments, targeting autonomous craft in restricted areas where international rules and regulations are not prioritised. This paper addresses the system engineering aspect of a highly complex system in which the seamless, predictable, and secure interoperability of vendorspecific hardware and software subsystems is a fundamental requirement for designing and implementing cyber-physical systems with artificial intelligence to assist or replace the navigating officer, such as autonomous marine surface vehicles. It addresses international rules in the sector and exhibits a system architecture that can fulfil the criteria for safe behaviour in foreseen occurrences and the capacity to request human aid if the autonomous system cannot manage a problem. The system thinking and engineering provided in this article have been applied to The GreenHopper, a harbour bus currently under construction and intended to undergo certification and enter commercial service.
Autonomous marine surface vehicles rely on computer systems with computer intelligence making decisions to assist or replace the navigating officer. A fundamental requirement for the design and implementation of such a cyber-physical system is seamless, predictable, and secure interoperability between vendor-specific hardware and software subsystems. The article describes a system design that includes mechanisms to mitigate the risks and consequences of software defects, individual component malfunction, and harmful cyber interference. It addresses international regulations in the field and demonstrates a system design that can meet the requirements for safe behaviour in foreseeable events while also having the ability to call for human assistance if the autonomous system is unable to handle a situation. The paper presents a design for highly automated vessels with several inherent risk-reducing features, including the ability to isolate and encapsulate abnormal behaviours, built-in features to support resilience to unexpected events, and mechanisms for internal defence against cyber-attacks. The article shows how this is provided by a novel middleware that supports risk mitigation, dependability, and resilience.
Autonomous systems strive to obtain salient features that include computer intelligence for obtaining situation awareness, decision support to a human navigator, or for facilitating autonomous decision-making in unmanned vehicles. This paper considers the case of autonomous marine surface vehicles, where high-quality decision support will be instrumental for obtaining a periodically unattended bridge and for approval of unmanned bridge operation with fallback through remote operation. The proposed design focuses on a sovereign-based architecture that facilitates safety, resilience and cyber-security. We address central elements of risk in the development and approval of autonomous systems; we analyze the challenges associated with testing, commissioning and maintenance of a highly complex cyber-physical system, and describe design principles for the sovereign agents architecture.
Autonomous surface vessels comprise complex automated systems with advanced onboard sensors. These help establish situation awareness and perform many of the complex tasks required for safe navigation. However, situations occur that require assistance by a human proxy. If not physically present on board, information digestion and sharing between human and machine become crucial to maintain safe operation. This paper addresses the co-design of on-board systems and a Remote Control Centre (RCC). Using the international regulations on watch-keeping (STCW) as a basis, the paper discuss how an autonomous system is designed to meet the STCW requirements. It is discussed how the autonomous system is made aware of the state of the vessel, its surroundings, on-board defects or navigational challenges and shared with the RCC in a collaborating system perspective.
This paper explores the potential of using green, autonomous ships in revitalizing inland shipping in Europe against the backdrop of declining market share and the dominance of "economy-of-scale" in waterborne freight transportation. It assesses the economic and environmental viability of converting freight from road to waterborne modalities in broader business ecosystems, specifically along the Rotterdam-Ghent corridor. The analysis leverages operational and commercial insights from logistics firms, ports and terminal operators, combined with data on European goods flows by road, and accounts for operational, financial and environmental variables including realistic scenario building and ecosystem implications. Findings indicate that inland shipping in general and green, autonomous shipping in particular offer both economically and environmentally viable alternatives to road transport. The study calls for further research into green, autonomous ships from an ecosystem perspective as a potential solution to current challenges in sustainable freight transportation.
A crucial component for unmanned underwater vehicles (UUVs), including remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs), are the thrusters, which, in addition, are sensitive to damage during operations in harsh environments. This paper presents a study on the impact of incipient faults on the performance of thruster propellers used in offshore operations. The study evaluates the reduction in propeller performance due to wear and tear under realistic working conditions. The study employs a combination of experimental data analysis and signal processing techniques, including fast Fourier transforms and harmonics analysis, to identify faults and assess their severity. The results show that worn propellers can be identified through 5th-order harmonics and rotational velocity changes. The paper concludes with a proposal for future research using a model-based approach to enhance fault detection capabilities further.
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.