Maritime Autonomous Surface Ships Research Articles

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.

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.

Towards an analysis framework for operational risk coupling mode: A case from MASS navigating in restricted waters

Maritime Autonomous Surface Ships (MASSs) constitute highly interconnected and tightly coupled multistate systems. Incorporating the coupling effects of both interactions and dependencies is centrally important to ensure navigation safety of MASSs. This paper proposes a framework for examining the coupling effects in the operational modes (OM) of MASSs. Failure Modes (FMs) of MASSs related to interactions with the environment and subsystems are identified using 24Model developed based on conventional ship accidents involving grounding in the west of Shenzhen port, China. FMs related to grounding are classified into the five risk types of human, organization, ship, environment, and technology, considering three OMs (states), i.e., manual control, remote control, and autonomous control. Based on this classification, the N-K model is applied to calculate the risk coupling values of scenarios involving multiple risk types. These scenarios are then ranked by the risk coupling values of the three OMs, and values are compared among different OMs. The results indicate that for grounding in the west of Shenzhen port most risk coupling values increase with increasing risk type diversity. Furthermore, the risk coupling mechanism in remote control differs significantly from that in manual control and autonomous control, thus risk management of MASS OMs should vary.

Optimizing anti-collision strategy for MASS: A safe reinforcement learning approach to improve maritime traffic safety

Maritime autonomous surface ships (MASS) promise enhanced efficiency, reduced human errors, and to improve maritime traffic safety. However, MASS navigation in complex maritime traffic presents challenges, especially in collision avoidance strategy optimization (CASO). This paper proposes a novel risk-based CASO approach based on safe reinforcement learning (SRL) with a reliability and risk hierarchical critic network (SRL-R2HCN) approach. Key steps in developing the approach start with the formulation of collision risk assessment. This is followed by the construction of a hierarchical network structure, supplemented by the supporting reward function, multi-objective function, and reliability measurement to realize the SRL-R2HCN. Finally, simulation experiments are conducted in mixed obstacle scenarios, and the results are compared with traditional algorithms to showcase the advancement and fidelity of the new SRL-R2HCN method. The results demonstrate that the proposed method can accurately assess collision risks in mixed obstacle scenarios and generate safe, efficient, and reliable collision avoidance strategies. The outcomes of this research provide a sound theoretical basis for the future development of MASS navigation safety and significant potential to improve the safe and efficient operations of MASS. Furthermore, the methodology could also benefit maritime transportation and shipping management.

Fuzzy Logic-Based Decision-Making Method for Ultra-Large Ship Berthing Using Pilotage Data

As seafarers are involved in Maritime Autonomous Surface Ships (MASS), except for those in the fourth level of autonomy, the decision making of autonomous berthing should be carried out and be understood by human beings. This paper proposes a fuzzy logic-based human-like decision-making method for ultra-large ship berthing, which considers locations, ship particulars and the natural environment, and these factors are treated as the input variables. The IF–THEN rules are then established after the fuzzification of the input variables and are used for fuzzy inference to derive the decision of ship handling. It can be implemented in the decision-making system for safe navigation or be included in the process of autonomous berthing. The pilotage data are collected with nautical instruments and a distance measurement system during the berthing process, which are used to validate the proposed model and calculate the speed and turn errors. The overall and individual error of the decision-making model is in a reasonable and small range, which indicates that the model has good accuracy. The results of this research offer theoretical and practical insights into the development of a human-like decision-making method for autonomous navigation in port waters and maritime safety management in the shipping industry. The model can be further applied to develop a more widely applicable decision-making system for autonomous navigation in confined waters.

Identifying the Optimal Valuation Model for Maritime Data Assets with the Analytic Hierarchy Process (AHP)

Data are becoming the most important factor in the development of the socio-economy, and data can be reevaluated as the owner’s valuable asset, which can increase the owner’s value. Therefore, each company is fiercely competing to secure data. Even in the marine field, maritime data are being produced exponentially, but it is difficult to expect more value creation because data are only stored rather than being used. This study used the analytic hierarchy process (AHP) methodology to select a suitable valuation model necessary to discover new values for maritime data. As a result of AHP analysis of 33 experts based on the stratified factors extracted from previous studies and expert opinions, the market approach (A2) was found to be the most suitable model. In addition, the most important factors to consider when selecting a valuation model were in the order of the characteristics of the maritime data (M1), the features of the maritime data market (M2), and the features of the maritime data valuation model (M3). The potential impact of this implementation could contribute to the establishment of an intelligent technology market by estimating the value of data and developing a platform for maritime data trading, allowing for more efficient data sharing and utilization by maritime autonomous surface ships (MASSs).

Challenges for the Education and Training of Seafarers in the Context of Autonomous Shipping: Bibliometric Analysis and Systematic Literature Review

The maritime industry is undergoing a profound transformation with the integration of autonomous technologies, which brings new challenges and opportunities for the education and training of seafarers. This article aims to examine the evolving landscape of autonomous ships and its impact on maritime education, with a focus on the changing roles and responsibilities of seafarers. The levels of autonomy defined by the International Maritime Organization (IMO) provide a framework for understanding the evolution towards fully autonomous ships and highlight the changing roles and responsibilities of seafarers. Using a systematic review based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA), this study examines maritime education for maritime autonomous surface ships (MASS). Using Scopus, Web of Science (WoS) and Google Scholar, a comprehensive search was conducted to identify relevant studies focusing on seafarer training and the impact of automation in the maritime sector. The analysis included bibliometric assessments, historical reviews and a categorization of research topics. This systematic review contributes to a deeper understanding of the current state and trends in maritime education for autonomous shipping. The findings inform educators and industry stakeholders about the critical aspects of education and training needed to address the challenges and realize the potential benefits of autonomous technologies in the maritime sector. The inclusion of bibliometric analysis enriches the study by providing a comprehensive overview of the researchers.

A hybrid human reliability analysis approach for a remotely-controlled maritime autonomous surface ship (MASS- degree 3) operation

Maritime autonomous surface ships (MASS) are one of the hot topics in maritime transportation even though they bring many challenges in terms of safety, security, and environment. This paper tackles the safety-related challenges of remotely controlled ships without seafarers on board but controlled at the shore. In this context, the reliability of the operator is of paramount importance for safe and efficient MASS operation. This paper performs systematic human reliability analysis for the operator of MASS (for degree 3) under the Bayesian belief network (BBN) and evidential reasoning (ER)- cognitive reliability and error analysis method (CREAM) approach. In the model, BBN is capable of determining the probability distribution of Contextual Control Modes in CREAM, while ER tackles the uncertainty and subjectivity of expert judgments. The outcome of the paper shows that the human reliability for remote control MASS operation is 8.88E-01. Besides its robust theoretical background, the paper will provide the utmost contributions to operators, managers, safety inspectors, and ship owners of MASS for safer and reliable operations.

A novel approach to hazard evaluation for MASS operation based on a hesitant fuzzy linguistic term set

Expert judgment has been widely utilized in ship risk assessment. The traditional method is believed to be capable of processing only designated linguistic variables, which fails to capture the degree of expert hesitation in assessing a complex system. A novel model, the bi-independent hesitant fuzzy linguistic term set (BHFLTS), based on the hesitant fuzzy linguistic term set (HFLTS), is proposed to incorporate this hesitation. This paper also identifies ten navigational hazards to maritime autonomous surface ship (MASS) operation based on a literature review. In addition, the BHFLTS approach combined with the technique for order preference by similarity to an ideal solution (TOPSIS) is utilized for hazard assessment and ranking, and with this method, “Collision avoidance decision algorithm malfunction”, “Ship situation awareness system failure” and “Command transmission system failure” are rated as the top three navigational hazards that threaten MASS operation.

Analysis of human errors in human-autonomy collaboration in autonomous ships operations through shore control experimental data

Human-autonomy collaboration plays a pivotal role in the development of Maritime autonomous surface ships (MASS), as Shore control center (SCC) operators may engage in the control loop by directly operating the MASS, or, in the supervisory loop, monitoring the MASS and taking over control when needed. Thus, effective human performance during takeover control and operation is crucial for the safety of MASS operations. However, since the MASS is still in the early phase of development, the mechanism of human errors is unknown, and the data on human-autonomy collaborative operation is scarce. Human reliability analysis (HRA) aims to assess human errors qualitatively and quantitatively, and is widely used in various complex systems to help safety analysis. This study is dedicated to incorporating advanced HRA methods elements to identify and quantify human errors during taking over control and operation of a MASS in collision avoidance scenarios. It presents virtual experimental results, combined with theoretical human error identification and assessment methods. At first, we apply the Human-System Interaction in Autonomy (H-SIA) method to identify potential human errors; secondly, we identify relevant Performance Shaping Factors (PSFs) including Experience, Boredom, Task complexity, Available time and Pre-warning, and performance measures of the human errors, and implement them in the virtual experiment based on a full-scale autonomous ferry research vessel called milliAmpere2. Finally, we build a Bayesian Network (BN) to present causal and probabilistic relationships between PSFs and human errors through experimental data. The results show that available time has the highest impact on takeover performance of operators, followed by task complexity and pre-warning. Boredom does not present a significant sole impact unless combined with available time. Experience does not show a significant impact on human performance. In addition to the relevance of the human errors analysis to the safe development and operational design of MASS, the developed method benefits other human-autonomy collaborative systems. The developed BN model shows adaptability to assess human error probabilities, and the practical significance of integrating experimental data into the existing HRA methodologies for complex systems.

Collision-free automatic berthing of maritime autonomous surface ships via safety-certified active disturbance rejection control

This paper addresses the automatic berthing of a maritime autonomous surface ship operating in a confined water environment subject to static obstacles, dynamic obstacles, thruster constraints, and space constraints due to shorelines. A safety-certified active disturbance rejection control (ADRC) method is proposed for achieving the automatic berthing task of an MASS in the presence of model uncertainties and ocean disturbances. An extended state observer (ESO) based on a second-order robust exact differentiator (RED) is employed to estimate an extended state vector consisting of internal model uncertainties and external ocean disturbances. With the aid of the RED-based ESO, a nominal ADRC law is designed to achieve the position and heading stabilization. To avoid collisions with static obstacles, dynamic obstacles, and shorelines, input-to-state safe high-order control barrier functions are used to guarantee safety. Optimized control signals are obtained based on a constrained quadratic programming (QP) problem within safety constraints. In order to translate the control signals into the individual thruster command, a constrained QP problem is further used to search for optimized commands in real time. It is proven that the closed-loop automatic berthing system is input-to-state stable. By using the proposed method, the MASS is able to reach the desired position and heading with collision avoidance. Simulation results verify the effectiveness of the proposed safety-certified ADRC method for automatic berthing.