The unmanned surface vehicle (USV) is usually required to perform some tasks with the help of static and dynamic environmental information obtained from different detective systems such as shipborne radar, electronic chart, and AIS system. The essential requirement for USV is safe when suffered an emergency during the task. However, it has been proved to be difficult as maritime traffic is becoming more and more complex. Consequently, path planning and collision avoidance of USV has become a hot research topic in recent year. This paper focuses on dynamic obstacle avoidance and path planning problem of USV based on the Ant Colony Algorithm (ACA) and the Clustering Algorithm (CA) to construct an auto-obstacle avoidance method which is suitable for the complicated maritime environment. In the improved ant colony-clustering algorithm proposed here, a suitable searching range is chosen automatically by using the clustering algorithm matched to different environmental complexities, which can make full use of the limited computing resources of the USV and improve the path planning performances firstly. Second, the dynamic searching path is regulated and smoothed by the maneuvering rules of USV and the smoothing mechanism respectively, which can effectively reduce the path length and the cumulative turning angle. Finally, a simulation example is provided to show that our proposed algorithm can find suitable searching range according to different obstacle distributions, as well as accomplish path planning with good self-adaptability. Therefore, a safe dynamic global path with better optimize performances is achieved with the help of multi-source information.

This paper proposes a method for the online parameter identification of nonlinear ship motion systems. First, the motion system of a ship is nonlinear, and in the course of sailing, the motion parameters of the ship will change with the change of the motion state of the ship and the sailing environment. To achieve the effect of real-time identification, we adopted an online receding horizon identification method. Second, identification parameters are the essential elements in the navigation control of intelligent merchant ships, and high-precision identification results can achieve better control effects. Therefore, we used an unscented Kalman filter (UKF) that has simpler mathematical structure and higher feedback efficiency than other identification algorithms listed in this paper, such as extended the Kalman filter, Kalman filtering and Ordinary Least Squares, as the identification scheme design algorithm, which is applied to ship motion system identification. Then, to solve the problem of significant identification errors in complex environments, we design a navigation identification framework combining a UKF and rolling wavelet denoising to realize the effect of the online identification of ships. Finally, a Korea Research Institute of Ships and Ocean Engineering (KRISO) Container Ship (KCS) was used for a self-navigation model experiment and data collection. The collected data and identification data were compared and analyzed. By comparing different identification algorithms before and after denoising, it was verified that the UKF algorithm proposed in this paper is superior relative to other traditional algorithms in identifying ship motion systems.

Ship weather routing has always been an important issue in the research field of navigation, and many scholars have been devoted to this research for a long time. To study the route strategies of different shipping companies, this paper proposes an improved multi-objective ant colony optimization (IMACO) algorithm based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). It can comprehensively consider ship navigation risk and fuel consumption cost under complex sea conditions. First, the grid method is used to model the marine environment. Then, we calculate the fuel consumption and the ship navigation risk of each grid and use the TOPSIS method to evaluate these two indicators for each grid. The results show that due to the different strictness of navigation management requirements of different companies, different routes are selected in the same sea area at the same time. Compared with the single-objective ant colony optimization (SACO) algorithm, the algorithm proposed in this paper can more comprehensively and effectively solve the problem of route strategy selection of shipping companies, which has great practical significance for ship operations management.

In recent years, ship motion control has been used in unmanned merchant ships. It is a key issue in the sailing performance and navigation cost of unmanned vessels. The merchant ship trajectory and attitude are always generated from the response mechanism for ship motion which uses one rudder and one propeller as the actuator. Therefore, it is a typical underactuated ship. However, there is not a specialized control test platform for unmanned merchant ships, in particular container ships. A precise merchant ship motion control platform is crucial; its computing power and motion attitude monitoring are difficult to solve by ship-borne computers. To solve this problem, a new free-running ship motion control platform is proposed, which realizes ship attitude monitoring and various motion control experiments. The proposed platform is novel as it embeds all the control elements in the ship motion model, which successfully solves the precise control of the ship motion control experiment with linear or nonlinear dynamics. In this paper, the design scheme of the ship motion control platform is first presented. Then, the operating principle of the platform is shown, and the adaptive model of the ship plane motion is selected. A series of free-running model experiments and simulations are conducted to test performance of the new platform. To this end, the proposed platform can meet the requirements.