Ship collisions pose significant risks to ship structures and the safety of lives on board. Rapid analysis of the extent of structural damage from collisions can provide a critical basis for emergency risk mitigation and damage control and rescue operations. While the Finite Element Method (FEM) can accurately calculate the degree of structural collision damage, it is time-consuming and requires numerous parameters, making it unsuitable for rapid damage assessment under limited input conditions. Using a dataset of 202 ship collision accidents that occurred between 2015 and 2019, this study applies statistical analysis to establish relationships between damage factors and damage levels. A Bayesian network model is developed to analyze the risk of collision damage grades under the combined influence of multiple factors. The results demonstrate that the proposed method agrees well with actual accident cases and can rapidly estimate the collision damage level of ship structures even when only limited parameters are available.

This study aims to apply deep reinforcement learning to address the challenges of trajectory planning and control for unmanned surface vehicles. In trajectory planning, the Q-learning algorithm is employed to generate trajectories in real-world aquatic environments. For the design of the reward function, factors such as shallow water areas are taken into account, with an emphasis on minimizing the number of turning points along the path. For trajectory tracking control, we integrate the Soft Actor-Critic (SAC) algorithm with the Proportional-Integral-Derivative (PID) control method to alleviate the difficulties of manual parameter tuning associated with conventional PID controllers. This hybrid approach also mitigates the interpretability limitations often found in pure deep reinforcement learning methods. Comparative experiments involving the traditional PID algorithm, Genetic Algorithm (GA), and Deep Deterministic Policy Gradient (DDPG) algorithm demonstrate the superiority of the proposed SAC-PID method. Simulation results show that the planned trajectories effectively incorporate multiple factors, including travel distance, shallow water regions, and number of turning point, the SAC-PID method achieves outstanding performance in trajectory tracking.

In response to increasingly stringent maritime emission regulations, vessels using alternative fuels—such as Liquefied Natural Gas (LNG) and methanol—are being gradually deployed. However, the number of ports offering bunkering for these new fuels remains significantly lower than those supplying conventional fuels, making route planning and operational management more challenging for alternatively fueled vessels. Shipping companies urgently need to rationally design routes and support the operation of alternative fuel bulk carriers within the context of scarce refueling infrastructure. To this end, this study develops a mixed-integer nonlinear programming (MINLP) model aiming to minimize total emissions, incorporating constraints such as total voyage duration and adjustable speed ranges. To address the nonlinear nature of the model, a heuristic algorithm based on variable neighborhood search is proposed to efficiently obtain near-optimal solutions. Finally, through case studies under both single-voyage and continuous-voyage scenarios, the collaborative optimization of refueling strategies and speed adjustments is thoroughly analyzed, demonstrating the effectiveness of the proposed model and algorithm.

In response to the global sulfur cap regulations established by the IMO MARPOL Convention and the management requirements of China's ship emission control areas, it is imperative to improve the efficiency and accuracy of maritime ship emission monitoring. This study proposes a three-dimensional "Terrestrial-Maritime-Aerial" monitoring technology for ship exhaust emissions, which integrates shore stations, bridges, ships, and mobile platforms into a unified network to address the practical monitoring demands of diverse and complex navigation environments. Additionally, an automatic peak signal recognition algorithm has been developed to enable intelligent remote monitoring of ship exhaust emissions and facilitate precise source tracing. Finally, an integrated intelligent control system was constructed, incorporating intelligent remote monitoring, precise source tracing, and enforcement verification, thereby promoting seamless information flow across multiple aspects of maritime supervision.

To address the high safety risks and frequent accidents associated with oil tanker loading and unloading operations, this paper proposes a data-driven risk assessment method based on Bayesian networks. Guided by systems engineering theory, a three-layer Bayesian network evaluation model comprising 34 nodes is constructed. Using the inference principle of the expectation-maximization algorithm, the conditional probabilities of the network nodes are computed to quantify risk levels within the model. The rationality and reliability of the model are verified through sensitivity and effectiveness analyses. Validation using data from 20 actual tankers demonstrates that the model's output aligns with risk levels assessed by port security personnel and can accurately evaluate the risks during oil tanker loading and unloading operations. The proposed model and method are applicable for assessing safety risk levels in oil tanker operations and can serve as a reference for safety evaluations of loading and unloading operations for other types of dangerous goods carriers.

Complex intersection waters, characterized by heavy ship traffic and frequent route crossings, exhibit complex traffic dynamics and a high risk of collisions, making route optimization in such areas highly important. Traditional route optimization methods tend to focus more on the formulation of traffic rules and traffic control measures. While effective, these approaches often rely on the subjective experience of maritime managers and lack an objective basis. To address these limitations, this paper proposes a route optimization method based on historical ship trajectory data. The ship traffic network is extracted through trajectory clustering and image processing techniques. A node similarity model is constructed, and a clustering algorithm is applied to partition the overall network into multiple local traffic networks. Route optimization is then achieved by merging nodes and reconstructing the network within each community. Experimental results demonstrate that the proposed method reduces the complexity of ship traffic by 50% and the risk of ship collisions by 30% compared to pre-optimized conditions. These improvements significantly enhance navigation safety, alleviate the regulatory burden on maritime authorities, and provide valuable insights for the planning of ship routing systems.

In view of the slow response speed of fuel cells, which limits their ability to promptly respond to dynamic power loads, a composite energy storage power supply is employed to address this issue. Using wavelet transform technology, the steady component of the load is allocated to the fuel cell, while the fluctuating portion is assigned to the composite power supply. Based on Pontryagin's minimum principle, an energy management strategy is formulated with the supercapacitor's energy as the state variable, the output power of the lithium battery as the control variable, and the root mean square current of the lithium battery as the cost function. A simulation model of the ship power system is built in Matlab/Simulink to validate the proposed energy management strategy. The results demonstrate that the proposed control strategy enables stable output power from the fuel cell and achieves rational power distribution according to the charge-discharge characteristics, capacity, and current state of charge of both the supercapacitor and the lithium battery. Compared to hybrid ships without supercapacitors and traditional fixed filter strategies, the proposed approach reduces the rate of current change in the lithium battery and extends the service life of the fuel cell and lithium battery.

Aiming to address the issues associated with traditional artificial cargo hold clearing operations, such as high risk, low efficiency and an insufficient level of automation, this paper introduces the Intelligent Cargo Hold Clearing Robot (ICHCR) system for bulk carriers. Based on dual-mode control, the ICHCR system achieves unmanned operation and robot-shore collaborative intelligent cargo hold clearing. The article presents an intelligent robot hardware system for cargo hold clearing and a cloud control platform for immersive operation. Intelligent control methods for the ICHCR are investigated, including perception and localization inside the cargo holds, and dual control modes of cloud control and autonomous navigation operation. Other core technologies include robot-shore cooperative cargo hold clearing. Finally, the system was applied and validated on a 70,000-tonne Panamax bulk carrier in a grain port. Experimental results demonstrate that the ICHCR system improves the safety of cargo hold clearing operations, optimizing the overall process and reduces time consumption, meeting the requirements of safety and efficiency.

To enhance the cognitive capabilities of waterborne transportation systems for intelligent ship navigation, this study first reviews the development of electronic map systems designed for the new generation of shipping systems and summarizes the technical requirements for future map functionalities in waterborne transportation. Subsequently, the Pan-information-based Navigation Scenario Map (PNSM) is proposed as a core component of the "Navigation Brain System". The elements, characteristics, and conceptual foundations of the PNSM are analyzed, with emphasis placed on its capabilities in object-oriented modeling and computer-cognitive representation of navigational environment data. Key technologies underpinning the PNSM are further discussed, including spatiotemporal object modeling of scenario elements, hierarchical organization of all elements, dynamic association among objects, adaptive cross-domain computing for scenario data, cognitive modeling of scenario semantics, intelligent information services, and multi-dimensional dynamic visualization of the scenario map. The application process and practical effectiveness of the PNSM are illustrated using data from the Intelligent Shipping Project in the Water Network Areas of Zhejiang Province, China. Research results demonstrate that the PNSM can effectively integrate, organize, correlate, and visualize water traffic data, enabling comprehensive cognitive representation of all elements within the waterborne transportation system and their interrelationships. It provides essential technical support for the advancement of next-generation shipping systems, including intelligent navigation technologies and standardized frameworks.

In order to reduce the emission of polluting gases from auxiliary generators during ship berthing, and in response to the growing adoption of shore power infrastructure, this paper incorporates the distribution of shore power into the berth scheduling plan of container terminals. Building upon the traditional berth scheduling model, relevant constraints for shore power allocation and carbon emission reduction targets are introduced, establishing a mathematical model that integrates ship-in-port activities, shore power usage, and carbon emissions. To solve the model effectively, an Improved Bat Algorithm (IBA) incorporating a stagnation mutation strategy is proposed. The inertia weight method is employed to update individual optimization speeds, preventing the algorithm from converging to local optima. Case studies show that considering shore power distribution increases the complexity of the berth scheduling problem. When the number of ships does not exceed 25, the mathematical model can be solved accurately with optimal solution quality; however, when the number increases to 30, the model cannot be solved within a reasonable time frame. In comparison, the IBA achieves efficient solutions for all test cases with significantly shorter computation times. The maximum deviation between IBA results and the exact model solutions is only 2.37%. Furthermore, compared to traditional Genetic Algorithms and the basic Bat Algorithm, IBA demonstrates superior performance in terms of solution quality and computational efficiency, with an average increase in computation time of only about 10 seconds compared to the basic bat algorithm. A matching analysis between the shore power retrofit ratio of berths and ships revealed that under a fixed dock berth retrofit ratio, terminal costs decrease as the ship retrofit ratio increases. However, once the two ratios reach equilibrium, the rate of cost reduction levels off and remains largely stable. These results indicate that optimal cost savings are achieved when the shore power retrofit ratios of berths and ships are appropriately matched. Ensuring a balance between supply and demand can effectively prevent resource waste and enhance the efficiency of shore power utilization.