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

Engineering notes

Compared to existing methods, the proposed approach significantly reduces collision-avoidance path length while maintaining safety, real-time performance, and COLREGs compliance.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

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.

Links and sources

• Official / arXiv page

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