Optimization of Ship Collision Avoidance Algorithms Under Uncertain Conditions of Target Ship Movement

Abstract In the intelligent navigation of maritime autonomous surface ships (MASSs), multiple sensors are used for vessels is unclear. F precise target positioning and navigational-obstacle detection. However, for collision avoidance, these vessels often face difficulties in continuously sensing obstacle signals, rendering the understanding of the navigational situation and potential encounter situations challenging. Such perceptual limitations hinder the effectiveness of existing collision avoidance algorithms. Therefore, this research focuses on optimizing these collision avoidance algorithms, particularly in situations where the navigational intent of obstructing target irst, to overcome the limitations of conventional velocity obstacle algorithms when managing long-duration collision avoidance tasks and making decisions in environments with dense, dynamic obstacles, a collision avoidance algorithm that combines the dynamic window approach and velocity obstacles has been proposed. This algorithm facilitates the creation of autonomous collision avoidance strategies for MASSs across various scenarios. Next, to tackle the problem of discontinuous output in the navigational state of the involved vessels, a CNN-GRU-based network is proposed for vessel state prediction. This network uses historical AIS data to predict the involved motion state and their uncertainties, obtaining an uncertainty distribution of the vessel's position. This ensures that even when moving obstacles cannot be continuously sensed, the considered intelligent decision-making system can provide effective collision avoidance support for the vessel. Subsequently, the vessel's domain is optimized, taking into full account the uncertainty in its position. Finally, a complete collision avoidance decision-making system architecture is designed for MASSs.