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.

Collision avoidance in maritime navigation, particularly between autonomous and conventional vessels, involves iterative and dynamic processes. Traditional path planning models often neglect the behaviours of surrounding vessels, while path predictive models tend to ignore ship interaction features essential in collision scenarios. This study proposes a decision-making framework for collision avoidance, particularly focused on the interaction between autonomous and conventional vessels. The framework integrates a human-preference-aware navigational trajectory prediction model into the collision avoidance process, enhancing the decision-making capabilities in dynamic and interactive environments. We first model human-controlled ship navigational preferences using a Long Short-Term Memory (LSTM) autoencoder combined with K-means clustering, by extracting key preferences from ship pairs identified through AIS data. These preferences, which reflect strategic trajectory adjustments in response to collision risks, are then incorporated into trajectory prediction using a Multi-Task Learning Sequence-to-Sequence (seq2seq) attention LSTM model. The predicted trajectories provide a basis for the decision-making framework, including a local path planner and a trajectory tracking controller, designed to dynamically adjust the predicted reference path for collision-free navigation and ensure its accurate tracking. The framework was validated using AIS data from the port of Rotterdam, identifying four distinct navigational preferences by combining an LSTM-autoencoder and clustering techniques and demonstrating improved prediction accuracy compared to other existing models. Simulation tests demonstrate that the framework utilises the predicted trajectories to inform decision-making, ensuring accurate path tracking while dynamically addressing collision risks for autonomous ships. By providing preference-aware and adaptive reference trajectories, the framework reduces the likelihood of MASS trajectory misinterpretation by conventional ships, thereby supporting proactive collision avoidance in mixed waterborne transport environments.

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.

Ammonia is considered one of the most promising hydrogen and energy carriers for decarbonizing deep-sea shipping and other remote heavy-duty applications. The AmmoniaDrive power plant concept uniquely combines Solid-Oxide Fuel Cell (SOFC) and Internal Combustion Engine (ICE) technology to address the issue of how to convert e-ammonia, produced from renewable resources, into useful on-board power safely and effectively, without the need for fossil fuels as combustion promotor. This paper introduces the AmmoniaDrive concept, outlines the challenging combustion properties of ammonia and ammonia-hydrogen mixtures and provides a short review of Compression Ignition ICE research for ammonia-fuelled engines. Three promising combustion concepts are introduced to give direction to further numerical and experimental research.

Multiagent reinforcement learning (RL) training is usually difficult and time-consuming due to mutual interference among agents. Safety concerns make an already difficult training process even harder. This study proposes a safe adaptive policy transfer RL approach for multiagent cooperative control. Specifically, a pioneer and follower off-policy policy transfer learning (PFOPT) method is presented to help follower agents acquire knowledge and experience from a single well-trained pioneer agent. Notably, the designed approach can transfer both the policy representation and sample experience provided by the pioneer policy in the off-policy learning. More importantly, the proposed method can adaptively adjust the learning weight of prior experience and exploration according to the Wasserstein distance between the policy probability distributions of the pioneer and the follower. Case studies show that the distributed agents trained by the proposed method can complete a collaborative task and acquire the maximum rewards while minimizing the violation of constraints. Moreover, the proposed method can also achieve satisfactory performance in terms of learning speed and success rate.

Effective operation and maintenance (O&M) management is significant for enhancing the economic performance of offshore wind farms. Despite recent research progress in O&M, there remains a gap in integrating health prognostics and spare parts inventory into decision-making processes at the scale of offshore wind farms. To bridge this gap, this paper develops an optimisation framework integrating these aspects to establish cost-effective joint maintenance and inventory policies. In the framework, a maintenance policy is firstly developed to plan maintenance actions based on component health and maintenance opportunities. Meanwhile, in order to support maintenance implementation, a multi-echelon inventory network using (s, S) policies is proposed to store diverse units across distinct warehouses. A genetic algorithm (GA) is then employed to identify the optimal policy, aiming to minimise overall costs. Upon developing the optimisation framework, in order to illustrate the application of the proposed approach in practice, a numerical simulation of a generic offshore wind farm in the North Sea is performed. Results demonstrate that comprehensive O&M management considering interrelationship between maintenance and inventory policies reduces overall costs, showcasing its capacity in strengthening the economic performance. Finally, sensitivity analysis is performed to investigate the most influential O&M factors, providing actionable insights for O&M management.

Offshore wind farms are a promising source of renewable energy, but they face significant challenges in terms of operation and maintenance (O&M). Traditional scheduling models often overlook the potential of condition-based maintenance (CBM). Addressing this gap, this paper introduces a novel framework, incorporating principles of Model Predictive Control (MPC), to optimize the O&M scheduling of offshore wind farms using prognostic-driven maintenance. The framework integrates probabilistic remaining useful life (RUL) prognosis in a mixed-integer linear programming (MILP) optimization model with a rolling horizon approach, in alignment with MPC’s predictive and adaptive decision-making approach. The optimization model determines the optimal time to replace each component by minimizing the expected cost over the expected lifetime. This approach seeks to achieve the lowest expense while guaranteeing the highest utilization rate of each component. For the case study presented, the total O&M costs are reduced by up to 15% with respect to corrective maintenance strategies.

Global synchromodal transportation is a promising strategy for providing efficient, reliable, flexible, and sustainable container shipping services across continents. It involves integrating multiple modes and routes owned by various operators to create a comprehensive transport plan. However, these operators often have their own local networks and are hesitant to cede control to a centralized platform. Instead, they prefer to share limited information in a coordinated manner to achieve a common goal without sacrificing their own benefits. This paper proposes a coordinated mechanism for global synchromodal transport planning, in which a global operator proposes incentives to local operators to select the most efficient modes and routes for shipping containers from one continent to another. An augmented Lagrangian relaxation approach is developed for the global operator to generate incentives, and a heuristic algorithm is designed to address the computational complexity of the optimization problems faced by local operators. We incorporate the proposed approaches with a rolling horizon framework to handle dynamic shipment requests received from spot markets and with a buffer strategy to address travel time uncertainties. The coordinated mechanism is tested on a real network between Asia and Europe, and results show that it can significantly increase total profits, reduce request rejections, and reduce infeasible transshipments compared to decentralized global transportation plans currently in use, particularly under scenarios with higher degrees of dynamism and uncertainty.

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.

Maritime Autonomous Surface Ships (MASS) have gained much attention as a safer and more efficient mode of transportation and a potential solution to reduce the workload of seafarers. Despite the highly sophisticated autonomous systems that enable MASS to make independent decisions, the presence of humans on board or in the loop of safety management highlights the need for effective human-machine interaction. However, a potentially systematic review of critical aspects of human-MASS interaction has not yet been conducted. In this paper, we aim to fill this gap by reviewing the literature related to human-MASS interaction from four crucial perspectives: the state of the art of human-MASS interaction, situational awareness for MASS, collision avoidance methods for MASS within a mixed waterborne transport system (MWTS), and human trust in MASS. Our review reveals that human-MASS interaction for safety and efficiency mainly focuses on four key aspects: (i) human factors, (ii) available technologies supporting the autonomy of MASS, (iii) system analysis and design for human-MASS interaction, and (iv) potential requirements regarding regulations. Moreover, we provide a detailed discussion of the three fundamental factors that influence human-MASS interaction, including situational awareness, decision-making for MASS in a mixed waterborne transport system, and human trust in the autonomous system of MASS. Finally, based on our analysis, we propose an integrated framework of human-MASS interaction in which these human factors are taken into account. We anticipate that these factors and their interaction will receive more attention to improve the safety and efficiency of MASS.

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.

Offshore wind energy is expected to be the most significant source of future electricity supply in Europe. Offshore wind farms are located far from the shores, requiring a fleet of various types of vessels to access sites when maintaining offshore wind turbines. The employment of the vessels is costly, accounting for the majority of the total O&M costs for offshore wind energy. Therefore, configuring the size and mix of the vessel fleet to support maintenance operations in a cost-effective manner is an issue of importance to enhance economics of offshore wind sector. In this paper, a discrete event simulation based model is proposed to present how a mixed vessel fleet with the specific configuration, including crew transfer vessels, field support vessels, and heavy lift vessels, performs maintenance for an offshore wind farm. The economic performance of the vessel fleet under a predetermined condition-based opportunistic maintenance strategy is investigated by using the model. A metaheuristic algorithm, simulated annealing, is employed to find the optimal fleet size and mix to make leasing decisions with the minimum costs. The performance of the developed approaches is evaluated by using a generic offshore wind farm in the North Sea. The sensitivity analysis is performed to investigate the most influential O&M factors.

Nowadays, many companies still conceive their logistic operations as a simple material replenishment of production plants and don’t invest money to structure their supply chain and make processes more efficient. In addition, the high complexity and the emerging uncertainties that are characterizing a more globalized, dynamic, and interconnected world stimulate businesses to innovate the management of their supplier network. Unexpected events, such as COVID-19 and the semiconductor crisis, have put companies in research for solutions that look to improve and strengthen the partnership with their suppliers. Digitization represents one of the most innovative and disruptive challenges in today’s supply chains. Indeed, the increasing amount of data retrievable from logistic and production processes today is yet not exploited enough in comparison with its potential benefits. Companies still work by silos and prefer to hide their information rather than sharing them with their partners. This research investigates the role of data visibility, in order to demonstrate its benefits in a complex supply chain. By collaborating with Ferrari on a Supplier Relationship Management (SRM) project, this paper presents the design of a supply chain control tower through Model Predictive Control. By simulating a Model Predictive Control (MPC) optimization model on a small part of Ferrari’s supplier network, the coordination, efficiency, and sustainability of the supply chain are assessed through a comparison with the current state and by evaluating the network’s performances in different logistic scenarios. Although this solution is presented as a decision-support tool, it is thought of as a key technology for the future development of autonomous supply chain operations.

In this study, we investigated autonomous vessel obstacle avoidance using advanced techniques within the Guidance, Navigation, and Control (GNC) framework. We propose a Mixed Integer Linear Programming (MILP) based Guidance system for robust path planning avoiding static and dynamic obstacles. For Navigation, we suggest a multi-modal neural network for perception, demonstrating the identification of obstacle type, position, and orientation using imaging sensors. Additionally, the paper compares an error-based PID control strategy and a Model Predictive Control (MPC) scheme as well. This evaluation aids in better evaluating their performance and determining their applicability within the GNC scheme. We detail the implementation of these systems, present simulation results, and offer a performance evaluation using an experimental dataset. Our findings, analysed through qualitative discussion and quantitative performance indicators, contribute to advancements in autonomous navigation and the control strategies to achieve it.

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.

The inland waterway transport sector is facing increasingly stringent legislation to reduce emissions and improve energy efficiency. Speed planning methods are an attractive option as they provide energy-efficient, timely and emission-reducing voyage planning for ships. However, current methods do not consider dynamic conditions of waterways and traffic. Due to these dynamic navigational conditions the static speed planning methods do not guarantee optimality, nor do they satisfy the constraints of the optimization problem throughout the journey. In this paper we propose an optimization structure that is based on the Model Predictive Control algorithm, which uses the most current information on water depth, water speed and expected delays to re-optimize the speed planning throughout the journey. Through a use case we show a 4.31% energy reduction compared to other speed planning strategies. Additionally, we show that the constraints regarding desired arrival times and safety are satisfied throughout the journey. Therefore, the method proves useful from a logistical, energy, emissions, and safety perspective. Modelling of uncertainties, lock interactions and predictions of waterway conditions will make our method an even more attractive option for speed planning.

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.

This paper presents an approach to the problem of collaborative collision avoidance of autonomous inland ships. We propose a distributed model predictive control algorithm that allows ships to negotiate their intention to collaboratively avoid collisions directly. Furthermore, a new method is introduced to better shape ships' behavior in order to follow Traffic regulations. The simulation results illustrate that the proposed algorithm could significantly increase ship safety navigation. Moreover, it also shows that the solution proposed by our algorithm complies with waterway Traffic regulations except in complex situations when other safe solutions are negotiated.

Cooperation between container transport service providers can increase efficiency in the logistics sector significantly. However, cooperation between competitors requires co-planning methods that not only give the cooperating partners an advantage towards external competition but also protect the partners from losing information, clients and autonomy to one another. Furthermore, modern freight transport requires real-time methods that react to new information and situations. We propose a real-time, co-planning method called departure learning based on model predictive control where a barge operator considers the joint cost of themselves and a truck operator when deciding barge departures. At regular time-intervals, the barge operator uses previous information to propose a number of departure schedules for which the truck operator discloses their corresponding expected operational costs. Co-planning thus only requires limited exchange of aggregate data. The impact of using departure learning on the transport system’s performance and the method’s learning quality are thoroughly investigated numerically on an illustrative, simulated, realistic hinterland network. With as little as six schedules being exchanged per timestep, departure learning outperforms decentralized benchmark methods significantly in terms of operational costs. It is found that using knowledge about the performance of related schedules is important for the exploration of opportunities, but if this is relied upon too much, the realized solution becomes more costly. It is also found that departure learning is a reliable and realistic co-planning method that especially performs well when peaks in the demand make departure times highly correlated to the cost of operating the transport system, such as in hinterland areas of ports which receive large container ships.

A novel follow-the-leader approach for azimuth-driven vessels is devised and experimentally tested in a model-scale outdoor scenario. The vessels are equipped with global navigation satellite and inertial navigation systems. A line-of-sight algorithm ensures the yaw-check ability of the follower vessel along the leader's path, while a speed-regulation allows to track its velocity. Track generation, guidance, navigation, and control modules are designed and assembled to be executed on-board in real time. The results of an outdoor experimental campaign are illustrated to show the effectiveness of the proposed approach.