The primary objective of this study is to investigate maritime collision avoidance and trajectory planning in the presence of dynamic and static obstacles during navigation. Adhering to safety regulations is crucial when executing ship collision avoidance tasks. To address this issue, we propose an optimized APF-ACO algorithm for collision avoidance and path planning. First, a ship collision avoidance constraint model is constructed based on COLREGs to enhance the safety and applicability of the algorithm. Then, by introducing factors such as velocity, position, and shape parameters, the traditional APF method is optimized, creating a dynamic APF gradient for collision avoidance decision making in the face of dynamic obstacles. Furthermore, the optimized APF method is integrated with the ant colony optimization algorithm, the latter modified to overcome the inherent local optimality issues in the APF method. Ultimately, validations are conducted in three areas: static avoidance and planning in restricted sea areas, avoidance under conditions of mixed static and dynamic obstacles, and avoidance in situations of multiple ship encounters. These serve to illustrate the feasibility and efficacy of the proposed algorithm in achieving dynamic ship collision avoidance while simultaneously completing path-planning tasks.

Predicting ship trajectories plays a vital role in ensuring navigational safety, preventing collision incidents, and enhancing vessel management efficiency. The integration of advanced machine learning technology for precise trajectory prediction is emerging as a new trend in sophisticated geospatial applications. However, the complexity of the marine environment and data quality issues pose significant challenges to accurate ship trajectory forecasting. This study introduces an innovative trajectory prediction method, combining data encoding representation, attribute correlation attention module, and long short-term memory network. Initially, we process AIS data using data encoding conversion technology to improve representation efficiency and reduce complexity. This encoding not only preserves key information from the original data but also provides a more efficient input format for deep learning models. Subsequently, we incorporate the attribute correlation attention module, utilizing a multi-head attention mechanism to capture complex relationships between dynamic ship attributes, such as speed and direction, thereby enhancing the model’s understanding of implicit time series patterns in the data. Finally, leveraging the long short-term memory network’s capability for processing time series data, our approach effectively predicts future ship trajectories. In our experiments, we trained and tested our model using a historical AIS dataset. The results demonstrate that our model surpasses other classic intelligent models and advanced models with attention mechanisms in terms of trajectory prediction accuracy and stability.

This study presents a novel image-based visual servoing fault-tolerant control strategy aimed at ensuring the successful completion of visual servoing tasks despite the presence of robotic arm actuator faults. Initially, a depth-independent image-based visual servoing model is established to mitigate the effects of inaccurate camera parameters and missing depth information on the system. Additionally, a robotic arm dynamic model is constructed, which simultaneously considers both multiplicative and additive actuator faults. Subsequently, model uncertainties, unknown disturbances, and coupled actuator faults are consolidated as centralized uncertainties, and an iterative learning fault observer is designed to estimate them. Based on this, suitable sliding surfaces and control laws are developed within the super-twisting sliding mode visual servo controller to rapidly reduce control deviation to near zero and circumvent the chattering phenomenon typically observed in traditional sliding mode control. Finally, through comparative simulation between different control strategies, the proposed method is shown to effectively counteract the effect of actuator faults and exhibit robust performance.

In this paper, the global stabilization problem of control systems with input saturation and multiple input delays is studied, and a new method is proposed to design nonlinear stabilization control laws. First, based on Luenberger’s canonical decomposition, the multiple-input delay system is transformed into a series of linear time-delay systems with single inputs and input saturation. However, for the converted system, each subsystem is coupled to the others. Therefore, the idea of recursion is adopted to construct a special state transformation with time delay for each subsystem and convert it into a linear system with time delay for both state variables and input variables. For the conversion system, a nonlinear controller with cascade saturation control is designed, and the controller includes some free parameters. The control performance of the controller is improved by adjusting the free parameters online. At the same time, a less conservative stability condition is established to ensure the dynamic performance of the closed-loop system. Finally, the effectiveness and superiority of the proposed method are verified by numerical simulation and practical applications in a spacecraft rendezvous system.

In addressing the high costs, inefficiencies, and limitations of purely digital simulations in maritime trials for unmanned vessel path planning, this paper introduces a ship virtual path planning simulation test system. This system, unbound by temporal and spatial constraints, vividly showcases the navigational performance of vessels. After analyzing the virtual testing requirements for the autonomous navigation performance of unmanned surface vehicles (USVs), we established the overall framework of this system. Data-driven by a numerical simulation platform, the system achieves synchronized operation between physical and virtual platforms and supports interactive path planning simulations between USVs and the virtual testing system. Furthermore, to address the limitations of traditional ship trajectory planning evaluation, this paper develops a global path planning fitness evaluation function that comprehensively considers trajectory safety, navigation distance, and vessel stability, achieving optimal comprehensive routes through the particle swarm optimization algorithm. Test results indicate an average roll reduction of 14.31% in the planned routes, with a slight increase in navigation distance. By integrating two-dimensional curve simulation with three-dimensional visualization, this paper not only overcomes the limitations of purely physical and purely virtual simulations but also enhances the overall credibility and intuitiveness of the simulation. Experimental results validate the system’s effectiveness, providing a novel method for autonomous navigation testing and evaluation of USVs.