IoT-Enabled Autonomous Maritime Navigation in Smart Ports: A Curriculum-Guided Shared Policy Learning Framework

• URL: http://arxiv.org/abs/2608.11597v1
• Date: Wed, 12 Aug 2026 03:08:53 GMT
• Title: IoT-Enabled Autonomous Maritime Navigation in Smart Ports: A Curriculum-Guided Shared Policy Learning Framework
• Abstract summary: This paper investigates onboard autonomous navigation for such IoT devices under partial observability and dense traffic conditions.<n>A curriculum-guided reinforcement framework with a shared recurrent policy is developed to enhance temporal reasoning, deployment scalability, and robustness of edge-level decision-making.<n>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.
• Score: 10.029939800011329
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• OpenFrontier: General Navigation with Visual-Language Grounded Frontiers [54.661157616245966]
  Open-world navigation requires robots to make decisions in complex everyday environments.<n>Recent advances in vision--language navigation (VLN) and vision--language--action (VLA) models enable end-to-end policies conditioned on natural language.<n>We propose OpenFrontier, a training-free navigation framework that seamlessly integrates diverse vision--language prior models.
  arXiv  Detail & Related papers  (2026-03-05T17:02:22Z)
• WaterVideoQA: ASV-Centric Perception and Rule-Compliant Reasoning via Multi-Modal Agents [23.828845891763617]
  We present WaterVideoQA, the first large-scale, comprehensive Video Question Answering benchmark specifically engineered for all-waterway environments.<n>We also introduce NaviMind, a pioneering multi-agent neuro-symbolic system designed for open-ended maritime reasoning.
  arXiv  Detail & Related papers  (2026-02-26T12:12:40Z)
• AutoFly: Vision-Language-Action Model for UAV Autonomous Navigation in the Wild [62.47761809929869]
  Vision-language navigation (VLN) requires intelligent agents to navigate environments by interpreting linguistic instructions alongside visual observations.<n>Current VLN research for unmanned aerial vehicles (UAVs) relies on detailed, pre-specified instructions to guide the UAV along predetermined routes.<n>We propose AutoFly, an end-to-end Vision-Language-Action model for autonomous UAV navigation.
  arXiv  Detail & Related papers  (2026-02-10T11:08:07Z)
• Designing Control Barrier Function via Probabilistic Enumeration for Safe Reinforcement Learning Navigation [55.02966123945644]
  We propose a hierarchical control framework leveraging neural network verification techniques to design control barrier functions (CBFs) and policy correction mechanisms.<n>Our approach relies on probabilistic enumeration to identify unsafe regions of operation, which are then used to construct a safe CBF-based control layer.<n>These experiments demonstrate the ability of the proposed solution to correct unsafe actions while preserving efficient navigation behavior.
  arXiv  Detail & Related papers  (2025-04-30T13:47:25Z)
• Vision-Based Deep Reinforcement Learning of UAV Autonomous Navigation Using Privileged Information [6.371251946803415]
  DPRL is an end-to-end policy designed to address the challenge of high-speed autonomous UAV navigation under partially observable environmental conditions.<n>We leverage an asymmetric Actor-Critic architecture to provide the agent with privileged information during training.<n>We conduct extensive simulations across various scenarios, benchmarking our DPRL algorithm against the state-of-the-art navigation algorithms.
  arXiv  Detail & Related papers  (2024-12-09T09:05:52Z)
• Evaluating Robustness of Reinforcement Learning Algorithms for Autonomous Shipping [2.9109581496560044]
  This paper examines the robustness of benchmark deep reinforcement learning (RL) algorithms, implemented for inland waterway transport (IWT) within an autonomous shipping simulator. We show that a model-free approach can achieve an adequate policy in the simulator, successfully navigating port environments never encountered during training.
  arXiv  Detail & Related papers  (2024-11-07T17:55:07Z)
• IN-Sight: Interactive Navigation through Sight [20.184155117341497]
  IN-Sight is a novel approach to self-supervised path planning. It calculates traversability scores and incorporates them into a semantic map. To precisely navigate around obstacles, IN-Sight employs a local planner.
  arXiv  Detail & Related papers  (2024-08-01T07:27:54Z)
• Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation [78.60496411542549]
  Connected and autonomous vehicles (CAVs) can reduce human errors in traffic accidents, increase road efficiency, and execute various tasks. Reaping these benefits requires CAVs to autonomously navigate to target destinations. This article proposes solutions using the convergence of communication theory, control theory, and machine learning to enable effective and secure CAV navigation.
  arXiv  Detail & Related papers  (2023-07-05T21:38:36Z)
• ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environments [56.194988818341976]
  Vision-language navigation is a task that requires an agent to follow instructions to navigate in environments. We propose ETPNav, which focuses on two critical skills: 1) the capability to abstract environments and generate long-range navigation plans, and 2) the ability of obstacle-avoiding control in continuous environments. ETPNav yields more than 10% and 20% improvements over prior state-of-the-art on R2R-CE and RxR-CE datasets.
  arXiv  Detail & Related papers  (2023-04-06T13:07:17Z)
• Path Design and Resource Management for NOMA enhanced Indoor Intelligent Robots [58.980293789967575]
  A communication enabled indoor intelligent robots (IRs) service framework is proposed. Lego modeling method is proposed, which can deterministically describe the indoor layout and channel state. The investigated radio map is invoked as a virtual environment to train the reinforcement learning agent.
  arXiv  Detail & Related papers  (2020-11-23T21:45:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.