Autonomous Surface Ships Research Articles

Trajectory tracking for autonomous surface ships using Gaussian process regression and model predictive control with BVS strategy

Trajectory tracking for autonomous surface ships using Gaussian process regression and model predictive control with BVS strategy

MASS and COLREGS–Adaptability, Challenges and Coping Strategies

Discussions surrounding Maritime Autonomous Surface Ships (MASS) are occurring in academia, industry, and the International Maritime Organization (IMO). Automatic collision avoidance, as one of the key technologies of MASS, is the core of the autonomous navigation function of MASS, and its role is to solve the autonomous collision avoidance problem during MASS navigation. Convention on the International Regulations for Preventing Collisions at Sea (COLREGS) is a fundamental basis for automatic collision avoidance, and the navigation of MASS should adhere to it. However, the applicability of MASS to COLREGS has not yet been resolved. Responding to these issues, the paper proceeds to analyze the principal challenges of MASS to COLREGS in terms of the application of MASS to good seamanship, the neglect provision, the lookout provision, the insight of one another provision, and the problems of the deviation provision. Furthermore, suggestions are put forth for the revision of COLREGS, including the mode of revision, the reconstruction of MASS collision liability, the long-term coexistence of MASS and traditional ships, and the risk control of COLREGS revision in the context of artificial intelligence. These suggestions aim to establish a foundation for a more effective adaptation to the advent of the MASS era.

Mechanisms for Securing Autonomous Shipping Services and Machine Learning Algorithms for Misbehaviour Detection

Technological developments within the maritime sector are resulting in rapid progress that will see the commercial use of autonomous vessels, known as Maritime Autonomous Surface Ships (MASSs). Such ships are equipped with a range of advanced technologies, such as IoT devices, artificial intelligence (AI) systems, machine learning (ML)-based algorithms, and augmented reality (AR) tools. Through such technologies, the autonomous vessels can be remotely controlled from Shore Control Centres (SCCs) by using real-time data to optimise their operations, enhance safety, and reduce the possibility of human error. Apart from the regulatory aspects, which are under definition by the International Maritime Organisation (IMO), cybersecurity vulnerabilities must be considered and properly addressed to prevent such complex systems from being tampered with. This paper proposes an approach that operates on two different levels to address cybersecurity. On one side, our solution is intended to secure communication channels between the SCCs and the vessels using Secure Exchange and COMmunication (SECOM) standard; on the other side, it aims to secure the underlying digital infrastructure in charge of data collection, storage and processing by relying on a set of machine learning (ML) algorithms for anomaly and intrusion detection. The proposed approach is validated against a real implementation of the SCC deployed in the Livorno seaport premises. Finally, the experimental results and the performance evaluation are provided to assess its effectiveness accordingly.

Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships—2nd Edition

This Special Issue, entitled “Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships—2nd Edition”, is the sequel to “Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships”, including five contributions [...]

A Risk Identification Method for Ensuring AI-Integrated System Safety for Remotely Controlled Ships with Onboard Seafarers

The maritime sector is increasingly integrating Information and Communication Technology (ICT) and Artificial Intelligence (AI) technologies to enhance safety, environmental protection, and operational efficiency. With the introduction of the MASS Code by the International Maritime Organization (IMO), which regulates Maritime Autonomous Surface Ships (MASS), ensuring the safety of AI-integrated systems on these vessels has become critical. To achieve safe navigation, it is essential to identify potential risks during the system planning stage and design systems that can effectively address these risks. This paper proposes RA4MAIS (Risk Assessment for Maritime Artificial Intelligence Safety), a risk identification method specifically useful for developing AI-integrated maritime systems. RA4MAIS employs a systematic approach to uncover potential risks by considering internal system failures, human interactions, environmental conditions, AI-specific characteristics, and data quality issues. The method provides structured guidance to identify unknown risk situations and supports the development of safety requirements that guide system design and implementation. A case study on an Electronic Chart Display and Information System (ECDIS) with an AI-integrated collision avoidance function demonstrates the applicability of RA4MAIS, highlighting its effectiveness in identifying specific risks related to AI performance and reliability. The proposed method offers a foundational step towards enhancing the safety of software systems, contributing to the safe operation of autonomous ships.

Letting losses be lessons: Human-machine cooperation in maritime transport

Navigation safety has been a critical guarantee of global shipping, and it becomes more challenging given the increasing employment of advanced technologies and novel ship design in the era of Maritime Autonomous Surface Ships (MASS). The human-centred risk analysis of human-machine cooperation is scarce in general and emerging in maritime transport in specific. This paper aims to develop a new approach enabling the analysis of significant risk influencing factors (RIFs) in human-machine cooperation through an in-depth investigation of the occurred mistakes and violations in the cooperative operations of seafarers and machines in maritime transport. Its novelties consist of (1) a novel approach to analysing and quantifying the connectivity between humans and machines in safety-critical operations, (2) new integration of the frequency and impact of RIFs in the human-machine cooperation model, and (3) ultilisation of graph theory to generate a network to analyse critical human-machine RIFs and their interactions with the system. The connectivity analysis of RIFs is conducted through a weighted undirected network, showing the features of RIF connectivity accommodating the closed-loop system. The proposed novel approach, which combines the frequency and impact features to identify critical RIFs and analyses graphical features, will aid to realise the human-centred risk analysis for MASS. The findings make contributions for ship designers to rationalise the clustering design of function-based automation and training organisations to improve seafarer skills by rationally considering the identified risk-based human-machine cooperation features, and providing new competence schemes that can fit the demands of MASS in future.

Gap analysis and harmonization of International Standards for Maritime Autonomous Surface Ships

Abstract This paper addresses the challenges and solutions associated with the implementation of autonomous ships, focusing on the Maritime Autonomous Surface Ships (MASS) Code developed by the International Maritime Organization (IMO). With the goal of adopting a mandatory goal-based MASS Code by 2030, this study examines the interoperability and standardization requirements necessary for the effective global operation of autonomous ships. The paper analyses the MASS Code in relation to existing international standards, identifies gaps, and proposes standards works to accommodate the emerging technologies. Additionally, it investigate on the application of Autonomous Navigation Systems (ANS) and introduces the concepts of Shore ANS and Shore Bridge to enhance remote control capabilities. The analysis emphasizes the need for comprehensive standardization to ensure the safe and efficient operation of autonomous ships in diverse maritime environments.

A Multi-Phase Mission Success Evaluation Approach for Maritime Autonomous Surface Ships considering Equipment Performance Degradation and System Composition Changes

With the development of Maritime Autonomous Surface Ships (MASSs), the mission success assessment problem cannot be ignored because of potential security issues of MASSs. This paper addresses the issue of performance degradation at different stages, the problem of state dependence in multi-stage missions of MASSs, and the correlation problem between missions of stages. Based on the coupling of the reliability model of each single-stage mission system, a multi-stage mission success evaluation method for MASSs is proposed. By leveraging the ability of the conditional probability tables in Bayesian networks to express the complex relationships between the nodes, the dynamic Bayesian network model of stages is constructed based on the fault tree. Based on the Markov process, the problem of state dependence on shared equipment between stages is solved. Considering the complex relationship between multi-stage missions, the virtual node is introduced, and the multi-state Bayesian network is combined to realize the coupling of the reliability evaluation results of each single-stage mission. It is applied to the multi-stage mission success evaluation of the MASS to obtain the success probability of MASSs and the key equipment of each stage. The results show that evaluation results are more suitable for engineering practice.

Safety Assurance of Maritime Autonomous Surface Ships

Abstract Safety of Maritime Autonomous Surface Ships (MASS) is inherently complex owing to the dynamic and unpredictable nature of the maritime environment. This is exacerbated by the pace of change in enabling technologies and their disruptive impact. Integral to the safety assurance of MASS is assessment and mitigation of risk. The International Maritime Organisation (IMO) defines Risk as “the combination of frequency and severity of the consequence”. The advent of software-controlled systems has introduced difficulties in quantifying frequencies; an issue which is more prevalent in the context of autonomy due to the complex interaction between control algorithms and the variabilities of the environment in which they are deployed. Within complex autonomous systems, therefore it is challenging to perform quantitative risk assessments using failure rates, which gives rise to a need for different techniques to assess risk. Novel risk assessment methodologies such as those presented in the European Maritime Safety Agency (EMSA) Risk Based Assessment Tool (RBAT) combined with conventional risk assessment techniques can offer a solution to this challenge. Compliance with legislation and standards is another cornerstone of safety assurance, which is also inherently difficult for MASS vessels owing to their having been developed for conventional vessels. A goal-based approach to legislation compliance could present an interim solution until the IMO goal-based Maritime Autonomous Systems (MAS) provides harmonisation across the industry. Whilst MASS Safety assurance is non-trivial, there are solutions to many of the challenges faced; this paper examines the predominant challenges and presents potential solutions.

Scenarios of Multiple Vessels Encounter for Safety Evaluation for Automatic Collision Avoidance Algorithm using AIS Data

Abstract Interest in Maritime Autonomous Surface Ships (MASS) is growing, and efforts are underway in Japan to realize them. One of the key issues in realizing autonomous operation is automatic collision avoidance. Simulation is effective in developing and evaluating such algorithms. This paper describes the generation of a set of simulation scenarios that consider the actual sea traffic using AIS data, which is traffic flow data in real seas. We assumed that the comprehensiveness of the scenarios could be ensured by generating scenarios by combining typical encounter patterns and collecting difficult situations that are encountered in real situations. First, the characteristics of the target sea area were identified from the AIS data, then difficult traffic situations were extracted from the data, and then a set of scenarios were generated based on them. Considering the presence of manned ships in the test area, we conducted some tests of the generated scenarios not only using fast-time simulations but also using a ship-handling simulator and conducted visual checks from own ship and the target ship.

Consideration of Simulation Scenarios for Safety Assessment of Heading, Speed and Track Control Function

Abstract MASS (Maritime Autonomous Surface Ship) has been under active development in recent years. Especially, a great deal of attention has been paid to the development of the system which automates the navigation task of a ship including situation awareness function, collision and grounding avoidance function and heading, speed and track control function. When considering a MASS for coastal or open sea voyages, verifying the heading, speed and track control function presents a significant challenge during harbour manoeuvring, because of including automatic berthing and unberthing. Thus, it is essential to establish a verification method through simulation technique. While numerous studies have been conducted to develop control algorithms, few have focused on safety assessment, especially in the creation of simulation scenarios. Therefore, this study proposes a method of creating simulation scenarios to assess the safety of heading, speed and track control function for MASS in harbour manoeuvring. There are two main issues. The first is how to set up the passage plan to be included in the scenario, and the second is how to provide environment conditions. To solve the initial issue, a classification of navigational modes is defined, and the possible manoeuvring patterns that can be performed in each mode are organised. To solve the second issue, the method of assigning wind speed and direction to the scenario is examined based on regulations and observational data. Finally, an illustrative scenario is presented to demonstrate the proposed method.

Preface

These proceedings contain the peer-reviewed full papers presented at the joint conference ICMASS (the 6th International Conference on Maritime Autonomous Surface Ships) and MTEC (International Maritime Port Technology and Development Conference), held October 29th – 30th, 2024, in Trondheim, Norway. MTEC and ICMASS bring together academia and industry from around the world showcasing cutting-edge research and development within maritime technology, autonomous ships and port operations. https://www.ntnu.edu/mtec-icmass2024/home Trondheim, Norway’s hub for technological innovation, has utilized its rich maritime heritage to position itself at the forefront of maritime innovation. With the world’s first test area for autonomous ships, its strong position within academia and research, Trondheim has gained a leading position in the field of maritime innovation through collaboration between maritime stakeholders. This year’s conference topics include: • Green shipping and ports • Smart and autonomous ship and port operations • Maritime safety • Maritime digitalization, connectivity and cybersecurity The theme of this year’s conference, featuring contributions covering topics such as AI & autonomy, cybersecurity, onboard control, remote & shore control, smart ships as part of smart logistics, green shipping, safety evaluations, collision avoidance, regulatory & legal frameworks, as well as discussions on liability, etc. The papers, published in these proceedings contain theoretical contributions as well as reports on practical implementations in field labs and industry, and the conference will also have oral only presentation. Totally there will be more than 90 presentations, where more than 60 papers are invited. A conference such as this can only succeed as a team effort, and, as such, we thank the International Program Committee and the Reviewers for their excellent work in reviewing the papers as well as their invaluable input and advice. On behalf of the organizing committee, List of MTEC/ICMASS-24 Organizing Committee, International Programme and Management Committee and International Reviewers are available in this Pdf.

Perform assessment of COLREGs onboard a maritime autonomous surface ship: Narrow Channels and Traffic Separation Schemes

Abstract The usage of Maritime Autonomous Surface Ships in coastal and heavily used maritime environments necessitates advanced navigation systems capable of fully complying with the International Regulations for Preventing Collisions at Sea 1972 and not only open sea encounters. A significant challenge in this domain is enabling autonomous vessels to understand and navigate complex collision avoidance scenarios dictated by Rule 9 Narrow Channels and Rule 10 Traffic Separation Schemes. These rules are not well covered by research and only partial solutions have been proposed. To address this, we propose a ruleset for a system that can blend the traffic situation and geometric constraints extracted from Electronic Navigational Charts in S-57 format. The proposed ruleset extends our previous work on operationalization of COLREGs by introducing spatial features describing the maritime geometries relevant for traffic situations in Narrow Channels and Traffic Separation Schemes. We apply the ruleset to various traffic situations, showing how the inclusion of geometric context improves decision-making by reducing ambiguity and therefore enables the assessment of traffic situations and ship encounters in the vicinity of Narrow Channels and Traffic Separation Schemes. Our work outlines how the integration of spatial features into a rule-based system enables autonomous navigation capabilities of MASS for safe operations and compliance with maritime regulations.

Risk Analysis on Autonomous Vessels based on Systems Theory – Application of NET-HARMS method

Abstract The shift towards Maritime Autonomous Surface Ships is a significant development in the maritime logistics industry, with the potential to enhance efficiency, safety, and environmental sustainability. However, the integration of autonomous systems also presents new challenges and risks, particularly in the absence of empirical data for traditional risk assessment methodologies. This research tackles this problem by utilizing the Net-worked Hazard Analysis and Risk Management System (Net-HARMS) method, a systems thinking method that hasn’t been previously employed in examining MASS. The method analyses the risks associated with the EC-funded, H2020, MOSES Project, which included a concept for automating the manoeuvring and docking processes with autonomous tugboats. The Net-HARMS method offers a comprehensive and holistic approach to risk assessment, overcoming the limitations of conventional probabilistic models. By constructing a Hierarchical Task Analysis and a task network, the research maps the system’s operational framework and explores task interdependencies. The use of a risk mode taxonomy allows for the identification of task-specific and emergent risks, which are then assessed by utilising the risk matrix of the Risk-Based Assessment Tool developed by DNV, to assess the final risk as a function of the effectiveness of each risk mitigation layer and the severity of the identified task consequences. The findings provide valuable insights into critical tasks requiring enhanced risk control measures and contribute to the development of safety constraints necessary for the successful implementation of autonomous shipping technologies. By applying Net-HARMS method to the realm of autonomous ships, this research not only fills a significant gap in maritime risk analysis but also sets a precedent for future studies in this rapidly evolving field.

Time-efficient model predictive control for autonomous tugs with adaptive input constraints

The marine autonomous surface ship (MASS) has gained significant attention because of its wide application in waterborne transportation in recent years. The trajectory control of the MASS is crucial in ensuring safety, particularly in narrow navigation areas and urgent encounter situations. Model predictive control (MPC) is well known as a sufficient method to solve the tracking problem considering the actuator saturation with model uncertainties and environmental disturbances. However, due to the constraints on computing resources and hardware limits, the controller may fail to find a feasible solution for MPC within a reasonable amount of time, especially when the system models are coupled and complex. To improve the solution efficiency, this paper introduces adaptive constraints to reduce search space for optimization, i.e., the current tracking point’s speed is transformed into input constraints in the MPC formulation to decrease the computation burden, as well as reduce mechanical wear on the thrusters with smaller amplitudes of control actions. Simulations are carried out to evaluate the effectiveness of the proposed method with different MPC forms.

COLREGs-Adaptive trajectory planning and decision-making in maritime autonomous surface ships

Decision-making for collision avoidance and trajectory planning are critical technologies for maritime autonomous surface ships. These systems must align with regulatory frameworks such as the International Regulations for Preventing Collisions at Sea (COLREGs) and account for the ship’s maneuvering capabilities for effective control and tracking. This study introduces a novel framework integrating regulatory consideration into the path-searching process, enhancing collision avoidance in both COLREG-compliant and non-compliant scenarios. The framework recasts the trajectory-planning problem into an optimal control problem and employs virtual obstacles and spatial–temporal navigation corridors consistent with collision-free decisions as constraints for trajectory optimization, improving navigation efficiency and comfort. The framework is validated through various encounter scenarios, and the results demonstrate that the proposed framework can produce superior collision avoidance decisions, while planning shorter navigation time and smoother collision-free trajectories, significantly improving the collision avoidance capability of maritime autonomous surface ships.

Transformative Impact of the EU AI Act on Maritime Autonomous Surface Ships

The International Maritime Organization collaborates with member states to develop the Maritime Autonomous Surface Ships (MASS) Code to establish an international agreement framework. In December 2023, an agreement was reached on the European Union (EU) Artificial Intelligence (AI) Act within the MASS Code. It was officially finalized and implemented on 1 August 2024. While the Act’s full application is set for two years later, certain provisions regarding safety against AI risks will take effect sooner. This study utilized a multiple-streams analysis method to examine how the EU AI Act impacted shipyards and shipping companies, focusing on ethical AI use and user safety. The findings indicate that technical completeness, safety enhancement, and AI system regulation through risk classification influence autonomous ship innovation. The challenges of reviewing the commercialization of new technologies for autonomous ships may result in market failures or decreased industry competitiveness. The study emphasizes the need to balance AI safety and ethical responsibility with autonomous ship development innovations. To ensure the sustainable application of AI technology, innovators must adapt to the EU AI Directive. Hence, this study confirms that the ethical and legal regulation of AI technology is crucial in technological innovation and development related to autonomous ships.

Trust in Automation (TiA): Simulation Model, and Empirical Findings in Supervisory Control of Maritime Autonomous Surface Ships (MASS)

Over the past three decades, Trust in Automation (TiA) has been the subject of extensive research. However, a large portion of the research takes a “static” approach to modeling trust and views trust as a linear unidirectional phenomenon. This view fails to recognize that trust is a dynamic construct that changes over time as an outcome of prolonged interaction with automation. The present study aims to address this gap and explore the nonlinear dynamic nature of trust by developing a simulation model of Trust in Automation (TiA) that can demonstrate trust evolution, deterioration, and recovery within the context of supervisory control of Maritime Autonomous Surface Ships (MASS). Employing System Dynamics (SD) approach, the model captures trust’s non-linear and reciprocal nature through dynamic feedback loops, producing behavioral patterns consistent with empirical observations of trust. The simulation results showcase the crucial role of initial trust conditions and the alignment of expectations with system performance in fostering trust and effective automation use. The study also explores the timing of system malfunctions, revealing that early faults have a greater negative impact on trust compared to later faults of the same magnitude. We tested a segment of the proposed model in an experimental study involving 30 human participants to investigate the effects of automation malfunctions on operators’ trust and behavioral responses during the supervisory control of MASS. Results not only validated the proposed model but demonstrated a significant decline in perceived reliability and trust in automation as well as the monitoring strategy after the automation malfunction.

Complexity Analysis Using Graph Models for Conflict Resolution for Autonomous Ships in Complex Situations

Abstract Maritime autonomous surface ships (MASSs) will reshape the fast-evolving ecosystem for their attractive socio-economic benefits and potential to improve safety. However, their new systems and technology need thorough verifications to identify unintended components of risk. The interaction between MASS cyber-physical systems and the existing regulatory framework is currently unpredictable; artificial intelligence-powered intelligent situation awareness and autonomous navigation algorithms must safely and efficiently adhere to the regulations which are only designed for human interpretation without MASSs consideration. This paper contributes to algorithmic regulations and particularly algorithmic COLREGs in real-world MASS applications. It focuses on codifying COLREGs into a machine-executable system applicable to MASSs, then analyzing their performance in dynamic and mixed interactions between multiple vessels in complex scenarios. Based on fullest pairwise COLREGs criteria, this paper considers decision-making (DM) and complexity analysis in multi-collision-conflict scenarios. Complexity influential factors are an interplay between the characteristics of COLREGs, traffic scenarios, MASS interactions, and the environment. Participant vessels are the decision-makers forming a decentralized uncertain DM process, casted into a multi-participant multi-conflict multicriteria DM problem. This is tackled through the technique of graph models for conflict resolution, using risk graph models and fuzzy preferences over alternative collision-avoidance states. This paper conducts a comprehensive analysis of DM and navigational complexity; we develop novel complexity estimation scores, tools for complexity monitoring for human intervention, and spatial analysis of traffic complexity for geo-intelligent MASSs deployment and operation planning. The presented work is validated on a database of historical scenarios extracted from multiple data sources.

Navigation safety of Maritime Autonomous Surface Ships

ABSTRACT Maritime Autonomous Surface Ships (MASS) present numerous advantages that can address various social issues associated with maritime transportation. To advance the realisation of MASS, institutional studies and demonstration tests are being conducted in multiple countries. The introduction of new technologies and systems in MASS will automate many tasks traditionally performed by seafarers, requiring proof that these innovations provide security and reliability on par with or superior to conventional vessels. Given that most vessels are used commercially, MASS must garner user support and demonstrate profitability. This study employs a systematic review methodology to examine the progress of MASS research, identifying its benefits and the challenges to ensure the safety of MASS operations. Additionally, the study assesses the impacts of MASS on various domains, including the economy, employment, vessel operations, cybersecurity, search and rescue, and human resource development. The necessary actions for the successful implementation of MASS are also outlined. This study proposes several key considerations for the safe navigation of MASS. Firstly, practical operations incorporating business aspects must be envisioned. Secondly, MASS should be equipped with automated facilities for accident response. Lastly, the roles and responsibilities of both human operators and automated systems must be clearly defined.