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.

With the rapid advancement of Maritime Autonomous Surface Ships (MASSs), developing intelligent decision-making systems that ensure navigation safety in complex waters has become a core priority for the maritime industry. To address the limitations of oversimplified ship dynamics, the instability in game-based strategies, and the presence of non-compliant ships in multi-ship encounters, a novel decision-making framework is developed based on Multi-Agent Deep Reinforcement Learning (MADRL). A three-degree-of-freedom (3-DOF) manoeuvring modelling group (MMG) model is incorporated to replace conventional constant-speed assumptions. By explicitly modelling the hydrodynamic forces acting on the hull, propeller, and rudder, the proposed framework captures the intrinsic coupling between the speed and heading, thereby ensuring that the generated manoeuvres conform to the physical and operational constraints. To achieve stable decision-making in multi-ship encounters, an MATD3-based decision-making module is integrated within a Centralised Training and Distributed Execution (CTDE) paradigm. This architecture enables ships to derive robust and decentralised policies, while benefiting from global information during training. In addition, the proposed method demonstrates a promising adaptability in the investigated multi-ship encounter scenarios. Simulations are conducted across a set of encounter scenarios restricted to open waters. The results demonstrate that the proposed framework achieves safe collision avoidance performance while generating COLREGs-consistent behaviours in basic encounters involving standard power-driven vessels under open-water conditions.