Hierarchical Motion Planning for MASSs: A COLREGs-Compliant Framework Integrating Offline Trajectory Optimization and Real-Time Coupled Course-Speed Decision Making

Autonomous path planning and collision avoidance (CA) in complex maritime environments are critical challenges for Maritime Autonomous Surface Ships (MASSs). This paper 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 collision avoidance with dynamic vessels, a multi-ship CA algorithm based on velocity prediction potential fields is introduced. A new collision-risk identification model is developed to ensure compliance with COLREGS and enable collaborative CA in complex multi-ship 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 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.