Search Results (3)

In ship navigation, determining a safe and economic path from start to destination under dynamic and complex environment is essential, but the traditional algorithms of current research are inefficient. Therefore, a novel differential evolution deep reinforcement learning algorithm (DEDRL) is proposed to address problems, which are composed of local path planning and global path planning. The Deep Q-Network is utilized to search the best path in target ship and multiple-obstacles scenarios. Furthermore, differential evolution and course-punishing reward mechanism are introduced to optimize and constrain the detected path length as short as possible. Quaternion ship domain and COLREGs are involved to construct a dynamic collision risk detection model. Compared with other traditional and reinforcement learning algorithms, the experimental results demonstrate that the DEDRL algorithm achieved the best global path length with 28.4539 n miles, and also performed the best results in all scenarios of local path planning. Overall, the DEDRL algorithm is a reliable and robust algorithm for ship navigation, and it also provides an efficient solution for ship collision avoidance. Full article

During the process of establishing the analytical quaternion ship domain model, the impact of ship traffic conditions and navigator state was not taken into consideration. However, the significance of these factors in the ship domain cannot be ignored. To create a more realistic representation of changes in the ship domain in real navigation environments, this study further considers the influence of ship encounter course, waterway traffic intensity, relative ship velocity, and the navigator state based on the quaternion ship domain model. As a result, a new dynamic quaternion ship domain model is proposed. To assess the changes in the size and shape of the ship domain under various navigation environments, ship domain scaling and shape transformation functions are introduced. Specifically, this study focuses on analyzing the ship traffic near the Lao Tie Shan Waterway, simulating the size and shape changes of the ship domain during the navigation process in this area. The findings indicate that the size of the ship domain dynamically adjusts to the traffic conditions. Additionally, when the navigator state is excellent, the ship domain takes on an irregular diamond shape with the smallest area, whereas when the navigator state is poor, the shape approximates a rectangle with the largest area. Furthermore, the dynamic quaternion ship domain model proposed in this study is compared to the ship domain models put forth by Goodwin, Davis, and co-authors. The results demonstrate that the dynamic quaternion ship domain model is more compatible and suitable for open waters compared to the static quaternion ship domain model. Full article

A two-stage ship path planning method is proposed, based on the Rapid-exploring Random Tree (RRT) algorithm, which is composed of global path planning and local path planning, addressing the important problem of finding an economical and safe path from start to destination for ships under dynamic environment, especially in waters with multiple obstacles and multiple target ships. The global path planning takes into consideration the ship draft and Under Keel Clearance to find navigable water using RRT, and reduces the path length and waypoints based on elliptic sampling and smoothing. In the local path planning, a dynamic collision risk detection model is constructed by introducing the Quaternion Ship Domain under a dynamic environment, and the restrictions of ship manoeuvrability and COLREGs are also involved. The simulation results show that the proposed model can find a satisfactory path within a few iterations, and keep clear of both static obstacles and dynamic ships. The research can be used to make and verify planned ship routes before sailing and to guide officers to make decisions regarding collision avoidance. Full article