A Robust Simulation Framework for Verification and Validation of Autonomous Maritime Navigation in Adverse Weather and Constrained Environments

• URL: http://arxiv.org/abs/2603.02487v1
• Date: Tue, 03 Mar 2026 00:25:09 GMT
• Title: A Robust Simulation Framework for Verification and Validation of Autonomous Maritime Navigation in Adverse Weather and Constrained Environments
• Abstract summary: Maritime Autonomous Surface Ships (MASS) have emerged as a promising solution to enhance navigational safety, operational efficiency, and long-term cost effectiveness.<n>Their reliable deployment requires rigorous verification and validation (V&V) under various environmental conditions.<n>This paper presents an enhanced virtual simulation framework to support the V&V of MASS in realistic maritime environments.
• Score: 7.743296200054435
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Maritime Autonomous Surface Ships (MASS) have emerged as a promising solution to enhance navigational safety, operational efficiency, and long-term cost effectiveness. However, their reliable deployment requires rigorous verification and validation (V\&V) under various environmental conditions, including extreme and safety-critical scenarios. This paper presents an enhanced virtual simulation framework to support the V\&V of MASS in realistic maritime environments, with particular emphasis on the influence of weather and bathymetry on autonomous navigation performance. The framework incorporates a high-fidelity environmental modeling suite capable of simulating adverse weather conditions such as rain, fog, and wave dynamics. The key factors that affect weather, such as rain and visibility, are parameterized to affect sea-state characteristics, perception, and sensing systems, resulting in position and velocity uncertainty, reduced visibility, and degraded situational awareness. Furthermore, high-resolution bathymetric data from major U.S. ports are integrated to enable depth-aware navigation, grounding prevention capabilities, and evaluation of vessel controllability in shallow or confined waterways. The proposed framework offers extensive configurability, enabling systematic testing in a wide spectrum of maritime conditions, including scenarios that are impractical or unsafe to replicate in real-world trials, thus supporting the V\&V of MASS.

Related papers

• DESCENT: Directed Edge Scene Encoding for Airport Surface Movement Prediction [54.94692733670454]
  DESCENT is a transformer-based architecture designed to handle heterogeneous dynamics and strict topological constraints.<n>Our approach features a Potential Reachable Set (PRS) context sampling mechanism that adaptively collects airfield environment context.<n>Our evaluations on the Amelia-10 benchmark demonstrate significant performance improvements over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-08-26T16:49:50Z)
• Hardware- and Vision-in-the-Loop Validation of Deep Monocular Pose Estimation for Autonomous Maritime UAV Flight [2.0935101589828244]
  This paper presents a hardware-validated vision-in-the-loop framework that enables fully autonomous indoor flight on ships.<n>The results establish a safe and hardware-realistic intermediate stage for developing maritime UAV autonomy prior to shipboard deployment.
  arXiv  Detail & Related papers  (2026-06-17T15:18:11Z)
• COLREGs Compliant Collision Avoidance and Grounding Prevention for Autonomous Marine Navigation [8.778606523987198]
  Maritime Autonomous Surface Ships (MASS) are increasingly regarded as a promising solution to address crew shortages, safety, and operational efficiency in the maritime industry.<n>This paper presents a unified motion planning method for MASS that achieves compliance with International Regulations for Preventing Collisions at Sea (COLREGs) and prevention requirements.
  arXiv  Detail & Related papers  (2026-03-03T00:11:26Z)
• 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)
• Depth-Constrained ASV Navigation with Deep RL and Limited Sensing [43.785833390490446]
  We propose a reinforcement learning framework for ASV navigation under depth constraints.<n>To enhance environmental awareness, we integrate GP regression into the RL framework.<n>We demonstrate effective sim-to-real transfer, ensuring that trained policies generalize well to real-world aquatic conditions.
  arXiv  Detail & Related papers  (2025-04-25T10:56:56Z)
• Learning Underwater Active Perception in Simulation [51.205673783866146]
  Turbidity can jeopardise the whole mission as it may prevent correct visual documentation of the inspected structures.<n>Previous works have introduced methods to adapt to turbidity and backscattering.<n>We propose a simple yet efficient approach to enable high-quality image acquisition of assets in a broad range of water conditions.
  arXiv  Detail & Related papers  (2025-04-23T06:48:38Z)
• MarineFormer: A Spatio-Temporal Attention Model for USV Navigation in Dynamic Marine Environments [3.669739954510407]
  We show that incorporating local flow field measurements fundamentally alters the nature of the problem, transforming otherwise unsolvable navigation scenarios into tractable ones.<n>We propose textbfMarineFormer, a Transformer-based policy architecture that integrates spatial attention for sensor fusion, and temporal attention for capturing environmental dynamics.
  arXiv  Detail & Related papers  (2024-10-17T18:57:15Z)
• Towards an Autonomous Surface Vehicle Prototype for Artificial Intelligence Applications of Water Quality Monitoring [68.41400824104953]
  This paper presents a vehicle prototype that addresses the use of Artificial Intelligence algorithms and enhanced sensing techniques for water quality monitoring. The vehicle is fully equipped with high-quality sensors to measure water quality parameters and water depth. By means of a stereo-camera, it also can detect and locate macro-plastics in real environments.
  arXiv  Detail & Related papers  (2024-10-08T10:35:32Z)
• Real-Time Multi-Scene Visibility Enhancement for Promoting Navigational Safety of Vessels Under Complex Weather Conditions [48.529493393948435]
  The visible-light camera has emerged as an essential imaging sensor for marine surface vessels in intelligent waterborne transportation systems. The visual imaging quality inevitably suffers from several kinds of degradations under complex weather conditions. We develop a general-purpose multi-scene visibility enhancement method to restore degraded images captured under different weather conditions.
  arXiv  Detail & Related papers  (2024-09-02T23:46:27Z)
• Angle Robustness Unmanned Aerial Vehicle Navigation in GNSS-Denied Scenarios [66.05091704671503]
  We present a novel angle navigation paradigm to deal with flight deviation in point-to-point navigation tasks. We also propose a model that includes the Adaptive Feature Enhance Module, Cross-knowledge Attention-guided Module and Robust Task-oriented Head Module.
  arXiv  Detail & Related papers  (2024-02-04T08:41:20Z)
• Vision-Based Autonomous Navigation for Unmanned Surface Vessel in Extreme Marine Conditions [2.8983738640808645]
  This paper presents an autonomous vision-based navigation framework for tracking target objects in extreme marine conditions. The proposed framework has been thoroughly tested in simulation under extremely reduced visibility due to sandstorms and fog. The results are compared with state-of-the-art de-hazing methods across the benchmarked MBZIRC simulation dataset.
  arXiv  Detail & Related papers  (2023-08-08T14:25:13Z)

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.