2025A-IS-3 Data-Driven Route Planning for Maritime Autonomous Surface Ships

抄録

Maritime autonomous surface ships (MASS) require route planning that ensures safety and compliance with the International Regulations for Preventing Collisions at Sea (COLREGs) under complex multi-ship and geographical conditions. However, designing explicit objective functions for multi-objective optimization remains challenging due to trade-offs among safety, efficiency, and rule compliance. This paper presents three data-driven approaches: inverse reinforcement learning (IRL), imitation learning (IL), and diffusion policy (DP), which learn directly from expert demonstrations without predefined objectives. Using simulator data from professional captains and real-world navigation data from the training ship Fukae-Maru, IRL inferred COLREGs-consistent reward functions, IL reproduced human-like collision-avoidance maneuvers, and DP generated feasible trajectories in congested waterways. The results demonstrate that learning-based planners can achieve COLREGs-compliant and human-acceptable navigation behaviors. The proposed framework provides a scalable foundation for extending autonomous navigation to cover remaining COLREGs rules and diverse local maritime regulations.