This paper investigates a robust cooperative path following control algorithm for an unmanned surface vessel and unmanned aerial vehicles (USV-UAVs) that releases the design complexity and command transmission requirements for the potential significance to implement a maritime square search mission. The latter is a classical search pattern suggested by the international aeronautical and maritime search and rescue (IAMSAR) manual. To program the reference signals for the USV-UAVs simultaneously, the reference routes are programmed that uses a virtual ship and a virtual aerial. Therein, the information of the virtual aerials can be mapped from a virtual ship. Further, the desired attitude angles are derived under the cooperative virtual guidance framework. For a control part, a novel threshold rule of the dynamic event-triggered mechanism is reconstructed according to output errors to avoid the artificial parameter tuning and reduce the data transmission load from the controllers to the actuators. Besides, a L-function is designed to eliminate an effect caused by the model nonlinearities and disturbances. This ensures a lower design complexity of the control system. The stability of the proposed control algorithm is proved via the Lyapunov theorem. Finally, the effectiveness and the reasonable are evaluated on the simulation platform.

In this paper, a prescribed performance path following control algorithm is presented for underactuated surface vessels (USV) that uses an output-based threshold rule and a shifting function to execute parallel (also called lawn mowing) maritime search missions. The latter consist of classical search patterns suggested by the international aeronautical and maritime search and rescue (IAMSAR) manual. The proposed scheme comprises a guidance and a control module. In the guidance module, a limited boundary-based guidance principle is developed with a position threshold rule to avoid continuous computation of heading reference signals. Associated with the guidance signal, a novel dynamic event-triggered mechanism is derived by designing an output-based dynamic rule without fixed parameters, which has the potential to reduce unnecessary communication resource usage between the controllers and the actuator systems when compared with existing techniques. A shifting function is introduced to ensure that the path following error is within a prescribed region. This will ensure proper balance between communication burden and tracking accuracy in the closed-loop system. Semi-global uniform ultimate bounded (SGUUB) stability of the proposed control system is guaranteed using Lyapunov theory. The advantages and effectiveness of the proposal algorithm are shown via simulations.

Artificial intelligence and Automatic Identification Systems (AIS) play pivotal roles in intelligent maritime navigation for the modern maritime industry. Many artificial intelligence maritime applications based on AIS data have dramatically benefited traditional operations and managements in the field of maritime industry, and also provided state-of-the-art predictive analytics for vessel collisions and route optimization. However, the problem of modeling the interactions of vessels in complex waters still needs to be adequately addressed. In this paper, we focus on using spatiotemporal AIS data to model and forecast multiple vessel trajectories amid dynamic interaction patterns, and we propose a forecast model based on a novel neural network, namely a spatiotemporal multi-graph fusion network (STMGF-Net). The innovative STMGF-Net comprises three crucial modules. First, a Spatiotemporal graph construction module generates interaction graphs of various navigation modes, such as motions, risks, and attributes of vessels, which can comprehensively capture the diverse interrelationships among various vessels. Second, a multi-mode fusion module embeds and fuses the above interaction graphs into STMGF-Net. Finally, squeeze-and-excitation and temporal convolutional networks are introduced as Squeeze-and-excitation temporal convolutional modules to enhance STMGF-Net optimization and performance. to enhance the overall efficiency of the model significantly. Overall, the STMGF-Net can recognize complex spatiotemporal interaction patterns among neighboring vessels so as to capture and integrate these interaction features for achieving high-precision prediction performance in intelligent maritime navigation. In numerical experiments, three water areas of Zhoushan Islands, Yangshan Waters, and Yangtze River Waters are used as training and testing datasets. The results show that STMGF-Net is superior improved prediction errors of average and final distance with increase of 49.637% and 50.622% than classic and state-of-art graph neural networks. to classic and benchmark-graph neural networks.

Accurate detection of sea-surface objects is vital for the safe navigation of autonomous ships. With the continuous development of artificial intelligence, electro-optical (EO) sensors such as video cameras are used to supplement marine radar to improve the detection of objects that produce weak radar signals and small sizes. In this study, we propose an enhanced convolutional neural network (CNN) named VarifocalNet * that improves object detection in harsh maritime environments. Specifically, the feature representation and learning ability of the VarifocalNet model are improved by using a deformable convolution module, redesigning the loss function, introducing a soft non-maximum suppression algorithm, and incorporating multi-scale prediction methods. These strategies improve the accuracy and reliability of our CNN-based detection results under complex sea conditions, such as in turbulent waves, sea fog, and water reflection. Experimental results under different maritime conditions show that our method significantly outperforms similar methods (such as SSD, YOLOv3, RetinaNet, Faster R-CNN, Cascade R-CNN) in terms of the detection accuracy and robustness for small objects. The maritime obstacle detection results were obtained under harsh imaging conditions to demonstrate the performance of our network model.

To improve the trajectory tracking performance of unmanned surface vehicle (USV), this article investigates the USV optimal control problem with the consideration of actuator wear. In the proposed algorithm, the USV control system is divide into kinematic subsystem and kinetic subsystem. In particular, corresponding performance indexes that looking forward to be optimized are defined for each subsystem. The related value functions, Hamilton-Jacobi–Bellman equations and optimal control policies are approximated by actor-critic neural networks. To reduce the wear of propeller and rudder, the event-triggered problem is considered as a zero-sum game solving problem, where the best control inputs and worst thresholds are delivered via minmax strategy. Also, the nonlinear uncertainties of the USV are approximated and environment disturbances are compensated in the value functions for better control performance. The USV closed-loop control system is proved semi-globally uniformly ultimately bounded stability via Lyapunov theory. Finally, a simulation case and harbor experiment are illustrated to verify the superiorities and engineering application values of the proposed algorithm.

To identify and screen the risk scenarios for the navigation risk of intelligent ships, the analysis and evaluation of navigational risks were performed in this study. Risk scenarios were developed and evaluated by mapping the hierarchical holographic modeling (HHM) into risk filtering, ranking and management (RFRM). In detail, considering the insignificant influences of some factors on navigational activities, risk factors were filtered and ranked using the RFRM model. Seven final factors were successfully determined, including traffic flow, navigation environment understanding, ship–shore interaction capabilities, target recognition capabilities, communication equipment reliabilities, professional skills, and situation judgments. The results indicated that cargo security can be guaranteed by following navigational risk identification and screening steps, and thus our findings provide theoretical guidance for the dynamic management of maritime organizations and ship companies. In addition, the proposed methodology is desirable for making predictions on maritime traffic risks.