Ship course-keeping control is of great significance to both navigation efficiency and safety. Nevertheless, the complex navigational conditions, unknown time-varying environmental disturbances, and complex dynamic characteristics of ships pose great difficulties for ship course-keeping. Thus, a PSO-based predictive PID-backstepping (P-PB) controller is proposed in this paper to realize the efficient and rapid course-keeping of ships. The proposed controller takes the ship’s target course, current course, yawing speed, as well as predictive motion parameters into consideration. In the design of the proposed controller, the PID controller is improved by introducing predictive control. Then, the improved controller is combined with a backstepping controller to balance the efficiency and stability of the control. Subsequently, the parameters in the proposed course-keeping controller are optimized by utilizing Particle Swarm Optimization (PSO), which can adaptively adjust the value of parameters in various scenarios, and thus further increase its efficiency. Finally, the improved controller is validated by carrying out simulation tests in various scenarios. The results show that it improves the course-keeping error and time-response specification by 4.19% and 9.71% on average, respectively, which can efficiently achieve the course-keeping of ships under various scenarios.

In order to effectively deal with collisions in various encounter situations in open water environments, a ship collision avoidance model was established, and multiple constraints were introduced into the velocity obstacle method, a method to determine the ship domain by calculating the safe distance of approach was proposed. At the same time, the ship collision avoidance model based on the ship domain is analyzed, and the relative velocity set of the collision cone is obtained by solving the common tangent line within the ellipse. The timing of starting collision avoidance is determined by calculating the ship collision risk, and a method for ending collision avoidance is proposed. Finally, by comparing the simulation experiments of the improved algorithm with those of the traditional algorithm and the actual ship experiment results of manual ship maneuvering, it is shown that the method can effectively avoid collisions based on safe encounter distances that comply with navigation experience in different encounter situations. At the same time, it has better performance in collision avoidance behavior. It has certain feasibility and practical applicability.

With the rapid development of autonomous collision avoidance algorithms on ships, the technical demand for the testing and verification of autonomous collision avoidance algorithms is increasing; however, the current testing of autonomous collision avoidance algorithms is mainly based on the virtual simulation of the computer. To realize the testing and verification of the autonomous collision avoidance algorithm in the real ship scene, a method of virtual reality fusion testing in open water is proposed and real ship testing is carried out. Firstly, an autonomous ship collision avoidance test and evaluation system is established to research the test method of ship encounters in open water. Starting from the convention on the international regulations for preventing collisions at sea (COLREG), the main scenario elements of ship collision avoidance are analyzed. Based on the parametric modeling method of ship collision avoidance scenarios, a standard test scenario library for ship collision avoidance in open waters is established. Then, based on the demand for a ship collision avoidance function test, the evaluation index system of ship collision avoidance is constructed. Subsequently, for the uncertainty of the initial state of the real ship test at sea, the virtual–real space mapping method to realize the correspondence of the standard scenario in the real world is proposed. A standardized testing process to improve testing efficiency is established. Finally, the method of conducting virtual simulation and virtual reality fusion tests for various scenarios are verified, respectively. The test results show that the test method can effectively support the testing of autonomous collision avoidance algorithms for ships in open waters and provide a practical basis for improving the pertinence and practicability of ship collision avoidance testing.