This paper proposes a greedy stochastic optimization algorithm for the sensor set decomposition used in the sensor fault monitoring of marine propulsion systems, based on fault isolability criteria. These criteria are expressed mathematically in terms of the number of unique columns in the theoretical fault signature matrices (FSMs) used during the sensor fault isolation process. Due to the large scale and complexity of marine propulsion plants, the diagnostic layer follows a distributed architecture with a combinatorial logic used for fault isolation in two cyber levels; the local and global decision logic. As a result, the FSMs of both levels are formulated as an integrated optimization problem. Each solution regarding the sensor set decomposition is then used to generate the respective distributed monitoring architecture, using semantic (qualitative) knowledge for the propulsion plant. Thus, the need for an analytical model of the plant is removed. Moreover, based on the design of the distributed monitoring architecture, the respective theoretical FSMs (quantitative) are Automatically generated and used for the evaluation of the objective function. Finally, simulation results are used to illustrate the application of the greedy stochastic optimization algorithm and its efficiency.

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This article proposes a distributed model-based methodology for the diagnosis of faults affecting multiple sensors used for condition monitoring and control of marine internal combustion engines (ICEs). To handle the complexity of the ICE, we consider it as a set of interconnected physical subsystems that constitute the physical layer. For every subsystem, the detection of sensor faults relies on the design of cyber agents, where every agent monitors one subsystem. To handle the heterogeneous dynamics of each subsystem in the fault detection decision-making process, each agent uses differential and algebraic residuals alongside adaptive bounds. For isolation purposes, a combinatorial decision logic is employed, realized in two cyber levels: the local and the global decision logic. The first aims at the recognition of all sensor fault patterns that might have affected the engine based on the local agent fault signatures and certain binary decision matrices. The latter is used to capture the propagation of sensor faults between the different monitoring agents. Simulation results are used to showcase the proposed methodology's efficiency in tackling the problem and its applicability.

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Autonomous inland shipping has great potential to enable intelligent and sustainable freight transport. At the same time, with the increasing traffic on confined waterways, ensuring safe operations of these autonomous inland vessels within limited operational spaces becomes imperative. This will require considering hydrodynamic effects during control design stages. This study presents a comprehensive analysis of an autonomous inland vessel’s manoeuvrability and controller design. The ship’s motions are modelled using an enhanced Manoeuvring Modelling Group (MMG) model to account for the hydrodynamic effects of inland waterways, including water depths, river currents, and bank effects. A verification study is conducted utilising a pusher-barge prototype model in shallow water to verify the model’s accuracy. Through the implementation of a controller, a course-keeping study is conducted to assess the vessel’s steering performance across various inland waterway scenarios, including sailing along river bends and waterway intersections. The results show that the manoeuvring model can generate fast and accurate vessel trajectory predictions. It is found that the proposed control technique proves effective in mitigating the confinement effects and countering disturbances caused by river currents, thereby ensuring efficient course-keeping suitable for the considered type of autonomous vessels on inland waterways.@en

As Autonomous Surface Vessels (ASVs) become increasingly prevalent in marine applications, ensuring their safe operation, in the presence of faults, is crucial to human safety. This paper presents a scheme that encompasses the detection and isolation of actuator faults within ASVs to ensure uninterrupted and safe operation. The method primarily addresses the loss of thruster effectiveness as a specific actuator fault. For fault detection, the proposed method leverages residuals generated by nonlinear observers, coupled with adaptive thresholds, enhancing fault detection accuracy. The active fault isolation strategy employs actuator redundancy to insulate specific system states from faults by dynamically reconfiguring the actuation configuration in response to detected faults. Comprehensive simulation results demonstrate the effectiveness of this methodology across diverse marine traffic scenarios where the ASV needs to perform a collision avoidance maneuver.

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Autonomous surface vessels (ASVs) have started to operate in many safety-critical scenarios where rich sensor information is required for situational awareness, environmental perception, motion planning, collision avoidance and navigational control. A timely diagnosis of faulty onboard sensors is therefore essential for ensuring maritime safety and reliability. In this paper, a model-based fault diagnosis scheme is presented for ASVs affected by multiple sensor faults. Various monitoring modules comprising nonlinear observers are employed for the detection of faults occurring in the vessel’s navigational sensors. Further, multiple fault isolation is performed based on a combinatorial decision logic, achieved by grouping the available sensors into multiple sensor sets. The efficacy of the proposed scheme is illustrated through a simulation example of a vessel trajectory tracking scenario. It demonstrates the scheme’s ability to effectively isolate multiple fault combinations impacting the sensors considered.@en

As inland waterway usage intensifies, it increasingly intersects and conflicts with railway and road transportation modes, highlighting the need for efficient management of these critical junctures. In particular, movable bridges represent a key intersection of highway and waterway Traffic but also a potential source of conflict. This paper proposes and analyzes the use of automatic schedulers that consider both highway and waterway Traffic, reduce conflicts, and make key decisions about bridge use. The METANET macroscopic Traffic model is elaborated to allow the simulation of drawbridge openings on the highway mainline, based on the modelling of mainstream metering. Several MPC-based schedulers are proposed using the designed highway Traffic model and key vessel information, aiming to study the impact of the bridge opening and scheduling on highway Traffic. The simulation results indicate a significant reduction in Traffic conflicts at drawbridge intersections due to the implementation of these schedulers. The functioning of the schedulers is shown to be robust and demonstrate verifiable behavior, indicating their high potential in real-world applications.

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This paper presents a rule-compliant trajectory optimization method for the guidance and control of Autonomous Surface Vessels. The method builds on Model Predictive Contouring Control and incorporates the International Regulations for Preventing Collisions at Sea relevant to motion planning. We use these rules for traffic situation assessment and to derive traffic-related constraints that are inserted in the optimization problem. Our optimization-based approach enables the formalization of abstract verbal expressions, such as traffic rules, and their incorporation in the trajectory optimization algorithm along with the dynamics and other constraints that dictate the system's evolution over a sufficiently long planning horizon. The ability to plan considering different types of constraints and the system's dynamics, over a long horizon in a unified manner, leads to a proactive motion planner that mimics rule-compliant maneuvering behavior, suitable for navigation in mixed-traffic environments. The efficacy and scalability of the derived algorithm are validated in different simulation scenarios, including complex traffic situations with multiple Obstacle Vessels.

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Autonomous surface vessels (ASVs) increasingly gain appeal in the maritime industry for their high efficiency and improved navigational capabilities. However, risks originating from various internal and external factors such as faults, traffic, harsh weather conditions, etc., can affect their guidance and control capabilities and impact nominal vessel operations. The existing risk mitigation methods mainly focus on the vessel's guidance system and do not consider unsafe actions due to the control system. In this paper, we propose a new method based on a partially observable Markov decision process (POMDP) model for the online risk mitigation of autonomous inland vessels. The POMDP model-based method utilizes information about situational awareness to assist the vessel's planning and control system in real-time decision-making during hazardous situations, thereby ensuring that the vessel remains in a minimum-risk condition. Based on the identified risk-influencing factors (RIFs), the transition probabilities are updated by a Bayesian belief network (BBN). A case study of an autonomous inland vessel navigating in a confined waterway is presented to demonstrate the capability of the proposed method.@en

The regulatory endorsement of the International Maritime Organization (IMO) and the support of pivotal shipping market players in recent years motivate the investigation of the potential role that autonomous vessels play in the shipping industry. As the complexity and scale of the envisioned applications increase, research works gradually transform the focus from single-vessel systems to multi-vessel systems. Thus, autonomous multi-vessel systems applied in the shipping industry are becoming a promising research direction. One of the typical research directions is floating object manipulation by multiple tugboats. This paper offers a comprehensive literature review of the existing research on floating object manipulation by autonomous multi-vessel systems. Based on the prior knowledge of object manipulation problems in multi-robot systems, four typical ways of maritime object manipulation are summarized: attaching, caging, pushing, and towing. The advantages and disadvantages of each manipulation way are discussed, including its typical floating object and application scenarios. Moreover, the aspects of control objective, control architecture, collision avoidance operation, disturbances consideration, and role of each involved vessel are analyzed for gaining insight into the approaches for solving these problems. Finally, challenges and future directions are highlighted to give possible inspiration.

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Transportation of a large offshore platform from inland waters to the open sea is a hazardous and challenging mission. With the development of the autonomous surface vessel (ASV), the problem of large floating object transportation has a chance to be solved by applying multiple physical-connected autonomous tugboats. This article proposes a distributed dynamic coordination control scheme for a multivessel autonomous towing system to transport an offshore platform under environmental disturbances. Where the dynamic coordination decision mechanism is based on the relative position of the two neighbor waypoints, the controllers are designed based on the multilayer model-predictive control (MPC) strategy with several specific cost functions, and the distributed control architecture is built based on the alternating direction method of multipliers (ADMM) with augmented Lagrangian function. The simulation experiment indicates that the proposed control scheme can achieve better consensus for the distributed control architecture accomplishment and more efficiently transport an offshore platform under environmental disturbances.

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Autonomous surface ships have become increasingly interesting for commercial maritime sectors. Before deep learning (DL) was proposed, surface ship autonomy was mostly model-based. The development of artificial intelligence (AI) has prompted new challenges in the maritime industry. A detailed literature study and examination of DL applications in autonomous surface ships are still missing. Thus, this article reviews the current progress and applications of DL in the field of ship autonomy. The history of different DL methods and their application in autonomous surface ships is briefly outlined. Then, the previously published works studying DL methods in ship autonomy have been categorized into four groups, i.e., control systems, ship navigation, monitoring system, and transportation and logistics. The state-of-the-art of this review paper majorly lies in presenting the existing limitations and innovations of different applications. Subsequently, the current issues and challenges for DL application in autonomous surface ships are discussed. In addition, we have proposed a comparative study of traditional and DL algorithms in ship autonomy and also provided the future research scope as well.

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To integrate and assist the system and automation design phases of complex marine vessels, this paper proposes a two-level semantically enhanced scheme. At the design level, the system components are described and automatically connected by a developed graph-making tool using semantic 'knowledge'. Decisions regarding the system selection are made based on certain Quality of Service Criteria (QoS) and enforced in the final semantic database using a dedicated cognitive agent. The automation level leverages the selected systems semantic information with that of the associated automation components and reuses the graph-making tool to update the connection graph. The resulting knowledge-graph is then used to 'reason' for the creation of feasible closed-loop control architectures while a cognitive agent determines which closed-loop architecture to use based on various QoS criteria. The chosen closed-loop architecture can then change in an online manner during the vessel operation in case that system reconfiguration is required either due to malfunctioning components, or aiming to satisfy mission's goals. The applicability and efficiency of the proposed method are shown using a case study for marine propulsion.

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This paper presents the design of a model predictive scheduling strategy to address the inland waterborne transport (IWT) problem considering bridges that must open to enable vessel passage. The main contribution is the formulation of a control-oriented model of the problem, including propositional logic expressions that characterize system behavior and their conversion into (in)equality constraints. The resulting model is embedded into a predictive scheduling approach to determine bridge opening timetables and vessel passage times in a coordinated manner. The effectiveness of the strategy is demonstrated on a realistic case study based on the Rhine-Alpine corridor.

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This paper proposes a virtual sensor scheme designed to compensate for sensor fault effects in marine fuel engines. The proposed scheme design follows a distributed approach, where the marine fuel engine is decomposed in several subsystems. Then, for each subsystem we design a monitoring agent that can actively compensate for the effects of sensor faults occurring in the specific subsystem. This is realized using virtual sensors that can estimate the sensor fault in order to reconstruct the faulty measurements. Due to the Differential-Algebraic mathematical description of marine fuel engine dynamics, we design three types of virtual sensors; using adaptive observers, Set Inversion via Interval Analysis (SIVIA) and static models. Simulation results are used to illustrate the efficiency of the method.

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This paper proposes a novel dynamic coordination control scheme for a physically connected multi-vessel towing system to transport an offshore platform. The transportation process is executed by four tugboats, and each of them has a leading or following role. To render the transportation faster, the roles of the tugboats can be switched in the towing process. The dynamic coordination decision mechanism is designed to allocate in real-time a combination of roles to the tugs by comparing the position and heading of the offshore platform to the next waypoint position. A control allocation strategy is developed to optimally control the position and heading of the tugboats considering multiple constraints. The reference trajectory of the tugboats is dynamically calculated based on the assigned role of each tugboat. A simulation experiment indicates that the proposed control scheme can enhance the maneuver-ability of the physically connected multi-vessel towing system and increase the efficiency of offshore platform transportation.

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This paper presents an adaptive approximation-based scheme for learning a partially known ship power propulsion plant under various environmental conditions. Considering the effect of water depth on the engine power, a dynamic model is defined comprised of the engine dynamics and the 1-DoF ship manoeuvring dynamics. The modelling challenge is the determination of ship resistance. To meet this challenge analytical modelling of ship resistance is combined with an error-filtering online learning (EFOL) scheme for computing an approximation of the unmodeled part of ship resistance related to wind and air. After simulations under multiple weather conditions, the trained model was demonstrated to efficiently estimate the unmodelled part of the ship resistance for an inland vessel.

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Sustainability goals have reignited interest in more fuel efficient modalities for urban freight transportation. The autonomous inland vessel has the potential to enable a modality shift to the waterway, however economically-conscious solutions to the technological challenges are lacking. This paper explores the potential role of stereovision technology as a primary sensor set within the application of autonomous inland vessels. The design of a multi-camera navigation system for mid-range perception and localisation tasks is presented with a view to providing sufficient data for collision avoidance tasks. The performance of these sensors in the application is evaluated with experimental testing using small-scale vessels in an indoor tank, demonstrating their capabilities and limitations for consideration in ongoing research.@en

This paper proposes a multi-objective cooperative control method for a ship-towing system in congested water traffic environments. The control objectives are to coordinate multiple autonomous tugboats for transporting a ship to: (i) follow the waypoints, (ii) adjust the heading, (iii) track the speed profile, and (iv) resolve collisions. The problem is tackled by the design of multiple control agents distributed in two control layers. Based on the strategy of model predictive control (MPC), the supervisory controller in the higher layer calculates the towing angles and forces of the ship, the tug controller in the lower layer computes the tug thruster forces and moment. The consensus between the lower and higher layer control is achieved by using the altering direction method of multipliers (ADMM) that makes the predicted tug position and heading approach to the desired tug trajectory. Simulation experiments indicate that the proposed method coordinates multiple autonomous tugboats to transport a ship smoothly and effectively and succeeds in multiple control objectives, in the meantime, the avoidance operation complies with COLREGS rules.

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This paper proposes a distributed model-based methodology for the detection and isolation of sensor faults in marine fuel engines. The proposed method considers a Mean Value First Principle model and a wide selection of heterogeneous sensors for monitoring the engine components. The detection of faults is realised based on residuals generated using nonlinear Differential Algebraic estimators combined with adaptive thresholds. The isolation of faults is, then, realised in two levels; local sensor fault detection and isolation agents are designed to monitor specific sensor sets and aim to detect faults in these sets; and a global decision logic is designed to isolate multiple sensor faults that may be propagated between the local monitoring agents. Finally, simulation results are used to illustrate the application of this method and its efficiency.@en

This paper addresses the problem of guaranteeing efficient inland waterway transport in the presence of sequential movable bridges, which must be operated to grant vessel passage. The main contribution is the formulation of the vessel passage scheduling problem as a mixedinteger programming problem. The scheduling algorithm receives vessel widths and voyage plans, and determines feasible vessel passage through bridges that best matches vessel plans. Process optimization is carried out using a rolling horizon implementation to keep the problem computationally tractable in real-time applications. Finally, a realistic case study based on the Rhine-Alpine corridor is used to test the approach and demonstrate its effectiveness.

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