Problem
Research questions and friction points this paper is trying to address.
autonomous maritime navigation
IoT-enabled devices
partial observability
dense traffic
smart ports
IoT-Enabled Autonomous Maritime Navigation in Smart Ports: A Curriculum-Guided Shared Policy Learning Framework
🤖 AI Summary
This work addresses the challenge of reliable navigation for IoT-enabled autonomous vessels in intelligent ports, where environments are partially observable and characterized by high traffic density. To tackle this problem, the authors propose a curriculum-guided shared recurrent policy reinforcement learning framework that integrates curriculum learning with shared recurrent neural networks. Operating under the centralized training with decentralized execution paradigm, the approach enhances temporal reasoning capabilities while ensuring deployment scalability. Experimental results demonstrate that the proposed method significantly improves navigation reliability, collision avoidance performance, and training stability across multiple realistic port simulation scenarios. Moreover, it exhibits strong generalization to previously unseen high-density traffic conditions.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
curriculum-guided reinforcement learning
shared recurrent policy
IoT-enabled autonomous navigation
edge intelligence
maritime collision avoidance
🔎 Similar Papers
No similar papers found.
