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This paper addresses the challenge of reliable onboard navigation for IoT-enabled autonomous maritime devices operating in congested waterways by developing a curriculum-guided reinforcement learning framework with a shared recurrent policy. The method enhances temporal reasoning and decision-making robustness while maintaining scalability for deployment in edge-level environments. Extensive simulations reveal that this approach significantly improves navigation reliability and collision avoidance compared to standard baseline methods, demonstrating effective generalization to high-density scenarios.
Curriculum-guided shared learning boosts the reliability and safety of IoT-enabled autonomous navigation in busy maritime environments.
As smart port infrastructures increasingly rely on autonomous maritime devices enabled by the Internet of Things (IoT), ensuring reliable onboard navigation intelligence has become a critical challenge for safe and scalable operations in congested waterways. This paper investigates onboard autonomous navigation for such IoT devices under partial observability and dense traffic conditions. A curriculum-guided reinforcement learning framework with a shared recurrent policy is developed to enhance temporal reasoning, deployment scalability, and robustness of edge-level decision-making. Centralized training is adopted as an offline design-time strategy, while all navigation actions are executed fully onboard, consistent with IoT edge intelligence paradigms. Extensive simulations in multiple realistic port environments demonstrate that the proposed approach improves navigation reliability, collision avoidance, and training stability compared with standard baseline methods, and generalizes effectively to previously unseen high-density scenarios. The results indicate that curriculum-guided shared learning provides a practical solution for scalable deployment of IoT-enabled autonomous maritime devices in smart port operations.
