To address the challenge of coordinated optimization between collision avoidance planning and motion constraints in the scenario where ships navigate close to dynamic surface targets, a hierarchical path planning method integrating the improved A^* algorithm, rapid reverse search iterative planning (RRSIP), and Hybrid A^* fine-grained planning, aiming to achieve efficient and kinematically compliant dynamic target tracking planning is proposed.

An improved A^* algorithm based on dynamic programming and oriented bounding box obstacle detection is used to plan a global reference path, reducing path length and redundant waypoints. For the dynamic target point, the RRSIP method is proposed, which reuses node information from prior searches via reverse search to quickly iterate and predict the approach point, avoiding global replanning and improving efficiency. Hybrid A^* algorithm is introduced near turning points for local refined planning, quickly generating a feasible path that satisfies ship kinematics and approach heading constraints.

Compared with other typical algorithms, the path length of the improved A^* algorithm proposed is reduced by an average of 4.17% and 1.79% respectively. The RRSIP method reduces the iterative planning time by at least 33.9% compared with the FR method. While ensuring path feasibility, the local Hybrid A^* planning reduces the time consumption by at least 72.1% compared with the global application.

The proposed method can effectively solve the problems of real-time performance and kinematic feasibility in dynamic target tracking, and significantly improve the autonomous tracking capability of ships in scenarios such as tugboat escort and maritime police law enforcement.

To overcome the limitations of the traditional random sample consensus (RANSAC) algorithm in cylindrical segmentation, a novel method is developed for segmenting point clouds of ring-ribbed shells by integrating structural features and statistical methods.

Initially, the model surface area feature is utilized to estimate the proportion of inliers, thereby enhancing the accuracy of initial parameters. Subsequently, principal component and radius constraints are introduced to enhance the accuracy of cylinder identification and reduce the number of iterations. Then, a weight function-based correction method is applied to mitigate outlier interference, thereby improving the accuracy of cylinder fitting. Finally, the DBSCAN algorithm clustered the point clouds of ring-ribs, and an improved RANSAC algorithm identified localized features, thus achieving precise measurement of component dimensions.

Experimental results show that the proposed method effectively addresses the intelligent recognition and dimensions measurement of components in various parts of the ring-ribs, significantly improving the recognition speed and accuracy of cylindrical shell and ring-ribs. The precision, recall, and overall accuracy of cylindrical shell reach 96.9%, 99.5% and 96.4% respectively, with a computational speed increase of approximately 4.6 times. The measurement error for ring-rib component dimensions is within 0.2%.

Compared with traditional methods, the proposed method offers significant advantages in the accuracy and computational efficiency of point cloud segmentation.

In order to improve the ship in the actual berthing path control accuracy and other issues,

a ship berthing path planning method is proposed based on nonlinear model predictive control (NMPC) combined with moving horizon estimation (MHE), enabling future trajectory prediction and real-time control updates. A Fossen model of the ship is established in the four-degree-of-freedom (4-DOF): heave, pitch, yaw and roll. It adapts to the movement state and environmental changes of the ship on the water surface and improves the accuracy of berthing control. By simulating the simulation experiment of autonomous berthing path planning of papua new guinea and manila international port ships.

The results show that the trajectory error is less than 8.0 m and the berthing position error is only 0.6 m. The effectiveness, generalization and applicability of the proposed algorithm are verified. In order to more accurately simulate the ship's motion response in waves, a 4-DOF ship mathematical model is developed to provide more comprehensive ship motion information for the control system. It further realizes more accurate control and enhances the robustness of the system.

The method provides theoretical and practical support for autonomous berthing in diverse port environments.

To review the current state of research on autonomous navigation decision-making and control technologies for intelligent unmanned surface vehicles, and to clarify the technical bottlenecks and development trends under scenarios of varying complexity,

a systematic investigation is conducted into the development history of key technologies for unmanned surface vehicles both domestically and internationally. It review addresses the differing technical requirements between low-to-medium complexity and high-complexity application scenarios, covering path planning, line-of-sight guidance, autonomous collision avoidance, automatic docking and undocking, multi-agent cooperative control, and autonomous recovery. It evaluates existing technological shortcomings and provides recommendations for future development.

Analysis indicates that autonomous navigation technology for open waters has matured and is gradually being implemented in engineering applications. However, core technologies for complex waters and complex missions still face developmental bottlenecks.

Looking further ahead, we propose establishing a standardized simulation and real-vessel testing evaluation system tailored to real-world scenarios. It will accelerate the rapid iteration and implementation of key technologies, thereby supporting the advancement of autonomous navigation decision-making and control technologies for unmanned surface vehicles in China.

To objectively and systematically understand the current status of reliability testing of maritime autonomous surface ships (MASS),

the current research status from three aspects: testing methods, testing technologies, and evaluation systems, and discusses the future development trends are analyzed. Specifically, it includes: conducting a visual analysis of 134 related papers using CiteSpace and VOSviewer to systematically sort out the research directions and development trends in the field of ship collision avoidance capability testing; sorting out the uses, advantages and disadvantages, and research status of the three major testing platforms: real ship testing, model testing, and virtual simulation testing; in-depth discussion on the development trends, feature comparisons, and challenges faced by the three mainstream testing scenario generation technologies based on expert knowledge, random sampling, and artificial intelligence; summarizing the evaluation indicators from four dimensions: data authenticity, scene complexity, risk, and generation efficiency; and on this basis, looking forward to future research directions.

The results show that virtual simulation testing has the advantages of low cost and high coverage and has become the main testing method. The ship collision avoidance capability testing method based on artificial intelligence has development potential in high-risk edge scenarios and ship interaction games, but the current research still faces challenges such as idealized motion models, lack of multi-ship dynamic game mechanisms, single evaluation indicators, and difficulties in virtual-to-real migration.

The research on testing scenario generation and deduction based on artificial intelligence has important research value and significance for promoting the testing of MASS.

In order to apply the dynamic inclinometers based on low-cost micro electro mechanical systems (MEMS) inertial measurement units to ships conviniently, it is necessary to overcome the significant impact of the ship sway and surge motion in moored condition, which are periodic acceleration changes of several seconds to tens of seconds, on inclinometer measurements.

The algorithm for inclinometers in the situation is studied. Gyroscope measurements are used for attitude quaternion update. Then the horizontal accelerometer measurement values without the effects of roll and pitch are obtained. On this basis, the error analysis and impact analysis of sway and surge is carried out. The horizontal accelerometer measurement values are put through a low-pass filter with zero-phase-delay. Then Kalman filtering is performed using the low frequency component of horizontal accelerometer measurement values as the observation of the Kalman filter, and horizontal attitude errors and angular velocity measurement errors as state variables. Attitude closed-loop correction is conducted to make the MEMS dynamic inclinometer keeping the expected accuracy in a long time when the ship is moored.

An experiment is conducted using a certain type of MEMS dynamic inclinometer to validate the algorithm of reducing the effect of sway and surge. The measurement accuracy reached 0.2°(1σ) with an acceleration amplitude of 0.8g in the experiment,

verifying the effectiveness of the algorithm. Key words: low pass filter; Kalman filter; dynamic inclinometer; sway and surge

In order to improve the efficiency of tugboat operation scheduling and energy utilization,

an improved intelligent load forecasting algorithm integrating dynamic data preprocessing and online learning is proposed. Based on a hybrid LSTM-Adaboost architecture, the algorithm addresses the issues of temporal feature degradation and multimodal data fusion through the integration of differentiated LSTM weak predictors and dynamic weight allocation (error sensitivity penalty mechanism), and designs an online learning trigger mechanism (automatic retraining based on prediction error threshold) to achieve dynamic model updating. Additionally, an environmental data collaborative optimization module, including tidal information, is introduced to enhance the adaptability of load forecasting to port conditions. The algorithm is compared with conventional LSTM-Adaboost to validate its effectiveness.

The results indicate that after iterative optimization, the mean squared error of the improved algorithm is reduced by 40.8% compared to the conventional LSTM-Adaboost algorithm, demonstrating higher prediction accuracy and environmental adaptability.

The research results can provide a reference for tugboat energy optimization, safety management, and intelligent scheduling in ports.

Offshore wind speed observations often suffer from data gaps, limiting the accuracy of wind resource assessment and wind farm operation.

A ratio-based interpolation method for reconstructing missing wind speed data using ERA5 reanalysis and floating LiDAR observations is proposed. Taking 100 m wind speed data from a coastal buoy as a case study, the method is evaluated across annual scale, seasonal variability, wind speed levels, and typical extreme weather events.

Results show that the method effectively captures temporal wind speed trends, with an annual average correlation coefficient of 0.839. However, it tends to underestimate wind speed magnitudes, with errors increasing notably under high wind conditions, especially during convective summer periods and typhoon events. Compared to traditional linear regression methods, the ratio method performs better in maintaining trends and controlling errors, and it demonstrates greater stability and robustness under conditions of severe wind speed fluctuations or extreme weather.

Overall, the ratio method demonstrates good applicability in stable wind environments and is suitable for long-term wind resource evaluation and data reconstruction. Nevertheless, its accuracy under extreme weather remains limited, suggesting the need for integration with high-resolution simulations or multi-source data fusion approaches.

To systematically review the technological evolution of unmanned surface vehicles (USVs) and explore the path of their convergence with intelligent ships, aiming to overcome the performance bottlenecks of individual USVs regarding endurance, computing power, and communication.

It reviews the centennial evolution of USVs, tracing the transition from radio remote control to fully autonomous navigation, and from single-agent operation to swarm collaboration. It provides an in-depth analysis of four core technologies: environmental perception, decision planning, motion control, and communication links. On this basis, the study focuses on the convergence trend between USVs and large intelligent ships, analyzing the "mothership-drone" cross-domain collaborative operational mode and the cloud-based management system driven by digital twins.

It indicates that current USV technology is undergoing an intelligent transition from "perception-avoidance" to "cognition-gaming". Furthermore, the "mothership-drone" collaborative mode, by combining the platform advantages of large ships with the high maneuverability of USVs, effectively resolves the challenges of individual USV operations in complex deep-sea environments and the "last mile" maneuvering difficulties for large intelligent ships entering and leaving ports, thereby achieving complementary advantages.

Collaborative mode represents a mainstream paradigm for future maritime operations. However, continuous breakthroughs are still required in areas such as regulatory adaptability, communication network security, and green energy propulsion. The findings provide theoretical references for constructing a new integrated air-surface-underwater intelligent maritime equipment system.

To effectively reduce fatigue damage, a reasonable dynamic cable design is required.

An optimization model based on an improved hybrid particle swarm optimization algorithm is established. It employ MATLAB to develop a genetic-chaotic particle swarm dynamic factor optimization algorithm and utilize the Orcaflex software for the overall design and optimization of platform dynamic cables. The optimization problem of deepwater dynamic cables is treated as the objective function, with parameters such as cable length, buoyancy block and counterweight block positions, and spacing as optimization variables. Building upon the foundation of the standard particle swarm algorithm and integrating genetic algorithms, it effectively prevent dynamic cable optimization parameters from falling into local optima. Chaotic initialization of initial particles is applied to ensure a uniform distribution in high-dimensional solution spaces. Dynamic inertia weight factors and learning factors are introduced to balance global and local search capabilities during optimization. Adhering to the Pareto principle, It formulate an objective function to facilitate multi-objective constrained optimization. The improved optimization algorithm shows better performance in terms of convergence, accuracy, and convergence speed.

It quickly and effectively balances the relationship between the maximum axial tension and the minimum bending radius of the cable and pipe, achieves the optimal design.

It provides strong support and guidance for practical engineering applications.