Jiao Liu

The research on decision-making models of ship collision avoidance is confronted with numerous challenges. These challenges encompass inadequate consideration of complex factors, including but not limited to open water scenarios, the absence of static obstacle considerations, and insufficient attention given to avoiding collisions between manned ships and MASSs. A decision model for MASS collision avoidance is proposed to overcome these limitations by integrating the strengths of model-based and model-free methods in reinforcement learning. This model incorporates S-57 chart information, AIS data, and the Dyna framework to improve effectiveness. (1) When the MASS’s navigation task is known, a static navigation environment is built based on S-57 chart information, and the Voronoi diagram and improved A* algorithm are used to obtain the energy-saving optimal static path as the planned sea route. (2) Given the small main dimensions of an MASS, which is easily affected by wind and current factors, the motion model of an MASS is established based on the MMG model considering wind and current factors. At the same time, AIS data are used to extract the target ship (manned ship) data. (3) According to the characteristics of the actual navigation of ships at sea, the state space, action space, and reward function of the reinforcement learning algorithm are designed. The MASS collision avoidance decision model based on the Dyna-DQN model is established. Based on the DQN algorithm, the agent (MASS) and the environment interact continuously, and the actual interaction data generated are used for the iterative update of the collision avoidance strategy and the training of the environment model. Then, the environment model is used to generate a series of simulated empirical data to promote the iterative update of the strategy. Using the waters near the South China Sea as the research object for simulation verification, the navigation tasks are divided into three categories: only considering static obstacles, following the planned sea route considering static obstacles, and following the planned sea route considering both static and dynamic obstacles. The results show that through repeated simulation experiments, an MASS can complete the navigation task without colliding with static and dynamic obstacles. Therefore, the proposed method can be used in the intelligent collision avoidance module of MASSs and is an effective MASS collision avoidance method.

It is important to accurately calculate flattening points when reconstructing ship hull models, which require fast and high-precision computation. However, some search algorithms, such as the bisection method, iterate near the optimal value too many times before converging in high-precision computation. The paper proposes a fast high-precision bisection feedback search (FHP-BFS) algorithm to solve the problem. In the FHP-BFS algorithm, the Newton–Raphson (NR) method is adopted to accelerate the convergence speed by considering the iteration characteristics of subintervals. Furthermore, a new feedback mechanism is proposed to control the feedback directions. In addition, an acceleration algorithm, called the interval reformation method, is used to guide the FHP-BFS algorithm for fast convergence. Finally, the flattening algorithm is improved by the FHP-BFS algorithm. In the comparative experiments, the practical efficacy of the FHP-BFS algorithm is first demonstrated, and then the optimal range of the threshold precision is determined. Next the FHP-BFS algorithm is compared to the best existing algorithms. Finally, the performance of the improved flattening algorithm is verified. The experiments demonstrate that the FHP-BFS algorithm has optimal performance among the compared algorithms, and it has an improved computation efficiency while maintaining robustness. The improved flattening algorithm reduces the computation time, ensures smoothness and meets practical engineering requirements.