Xiaoru Ma

Autonomous path planning and collision avoidance (CA) in complex maritime environments are critical challenges for maritime autonomous surface ships (MASSs). This article presents a novel hierarchical motion planning framework that integrates global and local path-planning algorithms. For global path planning in static-obstacle environments, a hybrid approach combining A*, Bezier curves, and particle swarm optimization (PSO) is proposed to enhance path search efficiency and ensure smooth trajectory optimization. For local path planning and CA with dynamic vessels, a multiship CA algorithm based on velocity prediction potential fields (VPPFs) is introduced. A new collision-risk identification model is developed to ensure compliance with COLREGs and enable collaborative CA in complex multiship scenarios. The framework also addresses environments with both dynamic and static obstacles through a dynamic path planner that integrates global and local planning. A key innovation is the dynamic CA decision making mechanism with alteration of course and/or speed (ACS), which provides robust real-time navigation strategies. Validation through simulations, real-ship model tests, and automatic identification system (AIS)-data analysis demonstrates the framework’s robustness and adaptability for MASS navigation. Compared to existing methods, the proposed approach significantly reduces collision-avoidance path length while maintaining safety, real-time performance, and COLREGs compliance. This work advances autonomous maritime navigation by addressing complex scenarios with a unified framework that balances efficiency, safety, and regulatory adherence.

Complex multi-vessel encounter situations are a challenging problem for ships to avoid collisions, and the International Regulations for Preventing Collision at Sea, 1972 (COLREGs) do not provide a clear delineation of multi-vessel encounter situations and the responsibility of collision avoidance (CA). Furthermore, Marine Autonomous Surface Ships (MASS), which realize autonomous navigation functions, face the problem of recognizing complex multi-ship encounter situations and the corresponding CA decisions. In this study, we adopt the velocity obstacle (VO) algorithm to visualize and identify the danger of multi-ship encounters with the own ship (OS) as the first viewpoint. Additionally, we consider the motion changes in target ships (TSs) and their possible CA behaviors as the basis of the ship’s CA decision-making. According to COLREGs, a simplified method for classifying the encounter situations of multiple clustered ships is proposed, considering the coupling of collision hazards and CA responsibilities between related TSs. On this basis, the corresponding CA decisions for each classified situation are proposed, and a large number of simulation experiments are conducted based on the proposed method by considering the three-ship and four-ship encounter model in the Imazu problem as an example. The experimental results indicate that the proposed method can effectively recognize the complex multi-ship encounter situation in the Imazu problem, and it can adjust the CA measures of the OS in time according to the COLREGs and the behavior of TSs. This provides the basis and reference for MASS when facing complex multi-ship encounter situations.