智能港口中基于物联网的自主海上航行：课程引导的共享策略学习框架

中文摘要 AI 辅助

随着智能港口基础设施越来越依赖由物联网（IoT）赋能的自主海上设备，确保可靠的机载导航智能已成为拥挤水道中安全和可扩展运行的关键挑战。本文研究了此类IoT设备在部分可观测性和密集交通条件下的机载自主导航，开发了一种带有共享循环策略的课程引导强化学习框架，以增强边缘级决策的时间推理、部署可扩展性和鲁棒性。采用集中式训练作为离线设计时策略，而所有导航动作均完全在机载执行，与IoT边缘智能范式一致。在多个真实港口环境中的大量模拟表明，与标准基线方法相比，所提方法提升了导航可靠性、避碰能力和训练稳定性，且能有效泛化到此前未见过的高密度场景。结果表明，课程引导的共享学习为智能港口运行中基于IoT的自主海上设备的可扩展部署提供了实用解决方案。

英文摘要

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.

发表机构

• Nanyang Technological University（南洋理工大学）
• School of Civil and Environmental Engineering（土木与环境工程学院）
• School of Mechanical and Aerospace Engineering（机械与航空航天工程学院）

机构由 AI 辅助整理，请以论文原文为准。