Autonomous inland shipping offers a safer and more efficient form of transportation over water with the potential to reduce maritime carbon emissions. However, the operation of autonomous vessels presents unique challenges due to complex dynamics, varying traffic conditions, and environmental disturbances. To ensure the safe navigation of these vessels in confined inland waterways, it is crucial to address manoeuvring prediction and motion control challenges. Research focusing on these challenges disregards or only partially incorporates inland waterway characteristics related to the vessel and its surroundings. This study provides a comprehensive analysis of these key factors. By modelling the vessel using a modified Manoeuvring Modelling Group (MMG) model specifically tailored for confined waterways, hydrodynamic effects due to shallow water, channel banks, and current are accounted for. A nonlinear model predictive controller (NMPC) is employed for the vessel path following control under various scenarios, including straight channels, confluences, and river bends. It is observed that the hydrodynamic effects from the channel banks significantly impact vessel steering. Compared to conventional proportional-integral-derivative (PID) controllers, NMPC effectively reduces course deviations and cross-track errors under varying water depth and ship-to-bank distance conditions, while also requiring fewer rudder deflections. Furthermore, key performance metrics related to the control of inland waterway vessels are proposed to evaluate the controller's performance further. The NMPC control law demonstrates its effectiveness in capturing the hydrodynamic effects and improving navigation safety in confined waterways.

This paper considers the presence of movable bridges in inland waterway transport, and presents a control framework for the joint dynamic coordination of bridge operations and autonomous vessel navigation to minimize waiting times of vessels at bridges. Simultaneous evolution of bridge occupancy and vessel position is captured by a control-oriented model that incorporates qualitative behavior in the form of propositional logic expressions. A model predictive control (MPC) strategy is designed considering adaptable bridge opening regimes to exploit vessel passage demand, and operational preferences of both vessel skippers and bridge operators are taken into account to reach fair trade-off decisions. A realistic case study pertaining to the Rhine-Alpine corridor is used to demonstrate the effectiveness of the approach. Appropriate key performance indicators (KPIs) are defined and employed for a quantitative comparison with a mixed-integer programming (MIP) strategy with fixed opening regimes. Furthermore, sensitivity to the main MPC parameters is examined by carrying out extensive testing to assess the effect of each design parameter on the solution.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 - known as COLREGs - relevant for motion planning. We use these traffic rules to derive a trajectory optimization algorithm that guarantees safe navigation in mixed-traffic conditions, that is, in traffic environments with human operated vessels. The choice of an 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 receding horizon. The ability to plan considering different types of constraints over a long horizon in a unified manner leads to a proactive motion planner that mimics rule-compliant maneuvering behavior. The efficacy of the derived algorithm is validated in different simulation scenarios.

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.

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.

This paper presents the design methodology of a mission-oriented modular control system for marine power plants. To this end, first power profiles, power plant layouts and control systems of multiple vessels such as tugboats, offshore support vessels, cargo ships and cruise ships are analyzed. By decomposing the power profile in two components, the propulsion and auxiliary power demand, the correlation between the power profile of a vessel and its mission is derived, and an algorithm that computes the power profile using mission and vessel data is proposed. Furthermore, the correlation between the power profile and the layout of the power plant is also investigated, with emphasis on how changes in the power profile result in power plant automation modifications. A modular secondary control level is then designed to cope with the required power plant automation modifications, by combining the Equivalent Consumption Minimization Strategy (ECMS) with Supervisory Switching Control (SSC). In this paper we consider battery modifications, following the example of Wärtsilä's ZESPacks. Simulation results are used to show the performance of the proposed switching control methodology, in relation to the stability of the components in the power plant after automation modifications occur. The main contribution of this paper is the novel approach for the secondary level power plant control system, introducing modularity to the otherwise assumed fixed layout of the power plant. Furthermore, the proposed algorithm can be used to determine the expected power profile for a new mission, and to identify required modifications of the power plant equipment.