Maritime Autonomous Surface Ships Research Articles

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.

Integrating situation-aware knowledge maps and dynamic window approach for safe path planning by maritime autonomous surface ships

This study investigates the enhancement of Maritime Autonomous Surface Ships (MASS) navigation and path-planning through the integration of ontology-based knowledge maps (KM) with the Dynamic Window Approach (DWA), a fusion termed KM-DWA. The ontology-based KM model is important for MASS navigation, offering a framework for situational awareness, including contextual information fusion and decision-making evidence. This research enriches the KM model with collision avoidance rules from the International Regulations for Preventing Collisions at Sea (COLREGs), building upon our previous work on MASS's efficient and COLREGs-compliant navigation in encounter scenarios. The model provides navigational context, covers COLREGs rules and environmental factors, and recommends MASS actions for various scenarios as suggested by COLREGs. Moreover, an adapted DWA, tailored to maritime navigation, accounts for specific constraints and safety measures for MASS, utilising KM-derived situational awareness as constraints in its cost function for path planning. A significant innovation introduced here is a tiered safety distance model featuring proactive, defensive, and collision buffers to ensure rule-compliant and effective collision avoidance. This scheme enables MASS to take timely collision avoidance actions at both proactive and defensive distances, in line with COLREGs recommendations. The effectiveness of the KM-DWA algorithm is validated by comparing it with the basic DWA algorithm in single- and multi-vessel encounter scenarios. The experiment outcomes illustrate the integrated approach's superiority in terms of COLREGs compliance and collision avoidance rate, emphasising its ability to support COLREGs-compliant decision-making and enhance situational awareness in autonomous maritime operations.

Improved Whale Optimization Algorithm for Maritime Autonomous Surface Ships Using Three Objectives Path Planning Based on Meteorological Data

In recent years, global trade volume has been increasing, and marine transportation plays a significant role here. In marine transportation, the choice of transportation route has been widely discussed. Minimizing fuel consumption, minimizing voyage time, and maximizing voyage security are concerns of the International Maritime Organization (IMO) regarding Maritime Autonomous Surface Ships (MASS). These goals are contradictory and have not yet been effectively resolved. This paper describes the ship path-planning problem as a multi-objective optimization problem that considers fuel consumption, voyage time, and voyage security. The model considers wind and waves as marine environmental factors. Furthermore, this paper uses an improved Whale Optimization Algorithm to solve multi-objective problems. At the same time, it is compared to three advanced algorithms. Through seven three-objective test functions, the performance of the algorithm is tested and applied in path planning. The results indicate that the algorithm can effectively balance the fuel consumption, voyage time, and voyage security of the ship, offering reasonable paths.

Application of Large Language Models and Assessment of Their Ship-Handling Theory Knowledge and Skills for Connected Maritime Autonomous Surface Ships

Maritime transport plays a critical role in global logistics. Compared to road transport, the pace of research and development is much slower for maritime transport. It faces many major challenges, such as busy ports, long journeys, significant accidents, and greenhouse gas emissions. The problems have been exacerbated by recent regional conflicts and increasing international shipping demands. Maritime Autonomous Surface Ships (MASSs) are widely regarded as a promising solution to addressing maritime transport problems with improved safety and efficiency. With advanced sensing and path-planning technologies, MASSs can autonomously understand environments and navigate without human intervention. However, the complex traffic and water conditions and the corner cases are large barriers in the way of MASSs being practically deployed. In this paper, to address the above issues, we investigated the application of Large Language Models (LLMs), which have demonstrated strong generalization abilities. Given the substantial computational demands of LLMs, we propose a framework for LLM-assisted navigation in connected MASSs. In this framework, LLMs are deployed onshore or in remote clouds, to facilitate navigation and provide guidance services for MASSs. Additionally, certain large oceangoing vessels can deploy LLMs locally, to obtain real-time navigation recommendations. To the best of our knowledge, this is the first attempt to apply LLMs to assist with ship navigation. Specifically, MASSs transmit assistance requests to LLMs, which then process these requests and return assistance guidance. A crucial aspect, which has not been investigated in the literature, of this safety-critical LLM-assisted guidance system is the knowledge and safety performance of the LLMs, in regard to ship handling, navigation rules, and skills. To assess LLMs’ knowledge of navigation rules and their qualifications for navigation assistance systems, we designed and conducted navigation theory tests for LLMs, which consisted of more than 1500 multiple-choice questions. These questions were similar to the official theory exams that are used to award the Officer Of the Watch (OOW) certificate based on the Standards of Training, Certification, and Watchkeeping (STCW) for Seafarers. A wide range of LLMs were tested, which included commercial ones from OpenAI and Baidu and an open-source one called ChatGLM, from Tsinghua. Our experimental results indicated that among all the tested LLMs, only GPT-4o passed the tests, with an accuracy of 86%. This suggests that, while the current LLMs possess significant potential in regard to navigation and guidance systems for connected MASSs, further improvements are needed.

The Evolution of Maritime Technology Development: A Dynamic Positioning System Perspective of Maritime Autonomous Surface Ship

The Evolution of Maritime Technology Development: A Dynamic Positioning System Perspective of Maritime Autonomous Surface Ship

An Improved NSGA-II Algorithm for MASS Autonomous Collision Avoidance under COLREGs

Autonomous collision avoidance decision making for maritime autonomous surface ships (MASS), as one of the key technologies for MASS autonomous navigation, is a research hotspot focused on by relevant scholars in the field of navigation. In order to guarantee the rationality, efficacy, and credibility of the MASS autonomous collision avoidance scheme, it is essential to design the MASS autonomous collision avoidance algorithm under the stipulations of the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs). In order to enhance the autonomous collision avoidance decision-making capability of MASS in accordance with the relevant provisions of COLREGs, an improved NSGA-II autonomous collision avoidance decision-making algorithm based on the good point set method (GPS-NSGA-II) is proposed, which incorporates the collision hazard and the path cost of collision avoidance actions. The experimental results in the four simulation scenarios of head-on situation, overtaking situation, crossing situation, and multi-ship encounter situation demonstrate that the MASS autonomous collision avoidance decision making based on the GPS-NSGA-II algorithm under the constraints of COLREGs is capable of providing an effective collision avoidance scheme that meets the requirements of COLREGs in common encounter situations and multi-ship avoidance scenarios promptly, with a promising future application.

Basic Study for Grasping the Human Gaze Movement of a Remoting-Ship Operator

Our research focuses on understanding the disparity in risk perception when operating maritime autonomous surface ships (MASS) between onboard and through screens. In our initial step, we conducted experiments using a ship simulator, involving 4 students aspiring to be navigators. They navigated through screens and participated in debriefing sessions. Human gaze movement was utilized to ascertain the ability to comprehend navigational risks solely through observation. Simultaneously, salivary alpha-amylase samples were taken to verify their accurate risk cognition. The key finding indicates that human gaze movement aids in precisely understanding navigational risks, suggesting the feasibility of future ship operations through screens. Our research targets navigators crucial in global logistics, aiming to enhance safety in remote ship operations by demonstrating that risk cognition is achievable through human gaze movement. This breakthrough is vital for the evolving realm of MASS operations.

Conversational user interfaces for maritime autonomous surface ships

We propose integrating conversational user interfaces (CUIs) into Maritime Autonomous Surface Ships (MASS) to facilitate direct communication with other ships without human intervention. The aim is to enable other ships and shore stations to inquire about and discuss the autonomous vessel's status, decisions, and future intentions in natural language, offloading VHF radio conversations for MASS operators. Recent advancements in Natural Language Processing (NLP) technology show promise. This paper presents an experimental study conducted in a ship simulator using a functional prototype CUI system. While the CUI system was capable of providing coherent answers to questions, the results indicate that interacting with the system is less trustworthy than interacting with a human ship officer. Key challenges contributing to this included: (1) a missing connection between the CUI and an autonomy system, leading to inaccurate and incorrect answers, (2) speech that lacked intonation and human-like qualities, and (3) a perceived inability of the system to collaborate. The paper discusses several aspects of utilising CUI systems in a maritime shipping environment and concludes by proposing an approach to develop and implement them safely.

Research on autonomous berthing control of MASS based on real time kinematic

Technologies for autonomous navigation of MASS (Maritime Autonomous Surface Ship) in open waters are advancing rapidly. However, the autonomous berthing technology, which represents the final phase of MASS achieving fully autonomous navigation, is still in the process of maturing. Consequently, the study of autonomous berthing control technology holds significant practical importance for the realization of fully autonomous navigation in MASS. This paper proposes an autonomous berthing control program for MASS, based on a MFAC (Model Free Adaptive Control) algorithm that integrates control technology and perception technology. The program encompasses the design of both the berthing logic algorithm and control algorithm. For the controllers, the Preview Deviation Yaw Control System and Speed Control System are designed using the CFDL-MFAC (Compact Form Dynamic Linearization based MFAC) algorithm. The RTK (Real Time Kinematic) data are utilized as inputs for the controller. The experiments are conducted in Lingshui Bay, Dalian, China. The results demonstrate that the lateral distance between the vessel's midship and the berth is less than 1.5 times the vessel's breadth, and the approaching angle is within the range of 0° to 10°, which shows that the proposed program could effectively help vessels achieve desirable berthing states.

Investigating the characteristics of skills and competency frameworks through a systematic literature review: a feasibility study to revise the STCW Code for seafarer training

ABSTRACT Recent research has provided a strong foundation to believe that future seafarers are expected to possess a traditional seafaring licence as a prerequisite for obtaining a licence to operate ‘smart’ ships [Maritime Autonomous Surface Ships (MASS)]. Since the standards of competence for a traditional seafaring licence are dictated by the Standards of Training, Certification, and Watchkeeping (STCW) Code, it may be implied that the Code may have to reinvent itself to include skills and competencies which will be relevant for the seafaring workforce of the future to take on expected roles and responsibilities. The question, thus arises, should the STCW Code continue in its current form in laying down the standards of competence, or should it draw lessons from skills and competency frameworks (used for the workforce of other industries) to outline defined set of behaviours deeply valued by the maritime industry? This question drove a systematic literature review, based on which, this paper presents findings to answer if past research had investigated the feasibility of the STCW Code to be translated into a skills and competency framework. This study also delved deeper into the essential characteristics of the frameworks which need to be imbibed (or which already exist) in the Code to continue allowing it to be relevant.

Developments and research directions for collision avoidance in mixed navigation environment for MASS: A systematic literature review with bibliometric analysis.

ABSTRACT: The integration of advanced technologies, data-driven processes, and automation under the Industry 4.0 revolution is fostering increased connectivity among ships and maritime infrastructures, particularly with the emergence of Maritime Autonomous Surface Ships (MASS) and the imperative for safe navigation. Despite existing studies delving into collision regulations (COLREG), autonomous navigation, and risk assessment for MASS navigation, a comprehensive systematic literature review and bibliometric analysis of academic research studies addressing collision avoidance in the context of MASS within mixed navigational environments has, to our knowledge, not been undertaken. This review aims to provide a concise overview of academic research advancements concerning the challenges associated with MASS and the safety of navigation in mixed navigational environments. To achieve this objective, we conducted a bibliometric analysis of pertinent studies, utilizing metrics and analysis tools to discern academic research topics, primary challenges, and directions in this domain. Our approach adhered to the principles outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) for systematic literature reviews, incorporating tailored keywords in a Scopus search. The results revealed that China, Norway, Poland, and South Korea are the leading countries in MASS collision avoidance, based on the weighted number of authors. Additionally, Ocean Engineering and the Journal of IFAC-Papers Online emerged as the principal journals for publications on this topic. Through the analyzed literature, specific challenges were identified, paving the way for suggested avenues for future research.

A PHM implementation frame work for MASS (Maritime Autonomous Surface Ships) based on RAM (Reliability Availability Maintainability)

A PHM implementation frame work for MASS (Maritime Autonomous Surface Ships) based on RAM (Reliability Availability Maintainability)

Risk assessment of maritime autonomous surface ships collisions using an FTA-FBN model

Maritime autonomous surface ships (MASS), presenting the future of maritime transport, are attracting increasing attention from the international maritime community. The collision risk analysis of MASS reveals unsolved challenges, which without appropriate solutions, will result in the error prone development of the relevant risk control measures and policies. Among the challenges, two significant ones in the existing literature are the lack of historical failure data to realise quantitative risk assessment, and 2) the complex causal relationship among the relevant risk factors. This paper aims to develop a new Fault Tree Analysis-Fuzzy Bayesian Network (FTA-FBN) model to conduct the collision risk assessment of MASS with uncertainty in data. First, it establishes a causal relationship among the risk factors through an FTA. Secondly, mapping the obtained FTA diagram into a BN allows fault diagnosis and the identification of the most important factors influencing MASS collisions. In this process, a survey is conducted to collect the primary data for configuring the conditional probabilities of the relevant influential factors and quantifying the developed BN for risk diagnosis and prediction. Finally, the new model is verified by using sensitivity analysis and three axioms and then applied to conduct scenario-based risk prediction and diagnosis to generate insightful findings to guide MASS navigation safety. The results demonstrate that the FTA-FBN model realizes the simplification of the expert scoring process, reduces computational complexity, and addresses the challenge of constructing causal relationships between MASS collisions and their risk factors due to the scarcity of historical accident data. Additionally, the BN backward reasoning identifies key collision risks, including external physical attacks, inadequate training of shore-based operators, insufficient maintenance of ship equipment and systems, and cyber-security threats. The new model when being adapted, can provide a reference for the formulation of safe navigation policies and provide important insights for shipping companies to ensure the safe navigation of their ships and shipbuilders to optimise ship design.

Systems-Based Safety Analysis for Hydrogen-Driven Autonomous Ships

In the maritime domain, hydrogen fuel cell propulsion and autonomous vessels are two important issues that are yet to be implemented together because of a few challenges. It is obvious that there are several individual safety studies on Maritime Autonomous Surface Ships and hydrogen storage as well as fuel cells based on various risk assessment tools but the combined safety studies that include hydrogen fuel cells on autonomous vessels with recent risk analysis methods are extremely limited. This research chooses the “System-Theoretic Process Analysis” (STPA) method which is a recent method for potential risk identification and mitigation. Both hydrogen and autonomous vessels are analyzed and assessed together with the STPA method. Results are not speculative but rather flexible compared to conventional systems. The study finds a total of 44 unsafe control actions (UCAs) evolved from human and central control unit controllers through STPA. Further, the loss scenarios (LS) are identified that lead to those UCAs so that loss scenarios can be assessed and UCAs can be mitigated for safe operation. The objective of this study is to ensure adequate safety for hydrogen fuel cell propulsion on autonomous vessels.

Collision avoidance for formation of autonomous ships using separation and recovery mechanism combine artificial potential field method

ABSTRACT It is essential to investigate the path planning and tracking problem for maritime autonomous surface ships (MASS). This study proposed an automatic collision avoidance method based on the separation and recovery mechanism with formation of MASS (F-MASS). First, the marine environment model utilized S-57 electric navigation chart (ENC) data for construction. The Scan-searching method was proposed for multipath and path-optimized redistribution method was developed to address MASS congestion. The formation pattern transformation was settled by separation and recovery mechanism. Considered the ‘unmanned–manned’ encounter situation, formulated avoidance strategies for both the MASS formation and manned ships encounters. Finally, the simulation experiment was carried out in the sea near Zhoushan port to verify the effectiveness of the algorithm. The results indicated that the method can satisfy the integrated operation of collision avoidance in a complex environment and can offer effective collision avoidance strategies for F-MASSs navigating in complex marine environments.

Многокритериальный выбор системы технической эксплуатации средств обмена данными морского автономного надводного судна

В статье рассмотрено применение метода анализа иерархий в контексте выбора стратегии технической эксплуатации оборудования обмена данными морского автономного надводного судна (МАНС) в условиях внедрения концепции а-Navigation. Разработана иерархическая модель и описан алгоритм принятия управленческих решений по организации технического обслуживания систем связи, целью которого является достижение и поддержание заданного уровня эксплуатационной надежности. Построена иерархическая структура для оценки качества предложенных альтернатив технической эксплуатации оборудования с учетом количественных критериев. Обоснован выбор критериев оценки предпочтительности стратегий технической эксплуатации и установлена шкала приоритетов критериев. Составлены матрицы парных сравнений альтернатив стратегий технической эксплуатации относительно каждого предложенного критерия. Произведена количественная оценка предпочтительности альтернатив стратегий технической эксплуатации. Сделан вывод о применимости математической модели метода анализа иерархий для объективной организации технической эксплуатации оборудования МАНС. The article discusses the application of the hierarchy analysis method in the context of choosing a strategy for the technical operation of data exchange equipment for a maritime autonomous surface ship (MASS) in the context of the implementation of the a-Navigation concept. A hierarchical model has been developed and an algorithm for making management decisions on organizing the maintenance of communication systems, the purpose of which is to achieve and maintain a given level of operational reliability, is described. A hierarchical structure has been constructed to assess the quality of the proposed alternatives for the technical operation of equipment, taking into account quantitative criteria. The choice of criteria for assessing the preference of technical operation strategies is justified and a priority scale of criteria is established. Matrices of paired comparisons of alternative technical operation strategies were compiled with respect to each proposed criterion. A quantitative assessment of the preference of alternative technical operation strategies was made. A conclusion is made about the applicability of the mathematical model of the hierarchy analysis method for the objective organization of technical operation of MASS equipment.

Trend Research on Maritime Autonomous Surface Ships (MASSs) Based on Shipboard Electronics: Focusing on Text Mining and Network Analysis

The growing adoption of electric propulsion systems in Maritime Autonomous Surface Ships (MASSs) necessitates advancements in shipboard electronics for safe, efficient, and reliable operation. These advancements are crucial for tasks such as real-time sensor data processing, control algorithms for autonomous navigation, and robust decision-making capabilities. This study investigates research trends in MASSs, using bibliographic analysis to identify policy and future research directions in this evolving field. We analyze 3363 MASS-related articles from the Web of Science database, employing co-occurrence word analysis and latent Dirichlet allocation (LDA) topic modeling. The findings reveal a rapidly growing field dominated by image recognition research. Keywords such as “datum”, “image”, and “detection” suggest a focus on collecting and analyzing marine data, particularly with deep learning for synthetic aperture radar imagery. LDA confirms this, with “image analysis and classification research” as the leading topic. The study also identifies national and organizational leaders in MASS research. However, research on Arctic routes lags behind that on other areas. This work provides valuable insights for policymakers and researchers, promoting a deeper understanding of MASSs and informing future policy and research agendas regarding the integration of electric propulsion systems within the maritime industry.

Advanced Bayesian study on inland navigational risk of remotely controlled autonomous ship

The arise of autonomous ships has necessitated the development of new risk assessment techniques and methods. This study proposes a new framework for navigational risk assessment of remotely controlled Maritime Autonomous Surface Ships (MASS). This framework establishes a set of risk influencing factors affecting safety of navigation of a remotely-controlled MASS. Next, model parameters are defined based on the risk factors, and the model structure is developed using Bayesian Networks. To this end, an extensive literature survey is conducted, enhanced with the domain knowledge elicited from the experts and improved by the experimental data obtained during representative MASS model trials carried out in an inland river. Conditional Probability Tables are generated using a new function employing expert feedback regarding Interval Type 2 Fuzzy Sets. The developed Bayesian model yields the expected utilities results representing an accident’s probability and consequence, with the results visualized on a dedicated diagram. Finally, the developed risk assessment model is validated by conducting three axiom tests, extreme scenarios analysis, and sensitivity analysis. Navigational environment, natural environment, traffic complexity, and shore-ship collaboration performance are critical from the probability and consequence perspective for inland navigational accidents to a remotely controlled MASS. Lastly, important nodes to Shore-Ship collaboration performance include autonomy of target ships, cyber risk, and transition from other remote control centers.