ASVSim (AirSim for Surface Vehicles): A High-Fidelity Simulation Framework for Autonomous Surface Vehicle Research

2025-06-27

AI Summary

To address the lack of high-fidelity open-source simulation platforms and synthetic datasets for unmanned surface vehicles (USVs) in port and inland waterway scenarios, this paper introduces ASVSim—the first waterborne navigation simulation framework extended from AirSim. ASVSim tightly integrates a six-degree-of-freedom (6-DOF) vessel dynamics model with realistic marine sensor simulations, including radar, RGB, and depth cameras. It enables scalable generation of photorealistic synthetic data with precise, pixel-accurate annotations, supporting both classical control design and deep learning—including reinforcement learning—algorithm development and validation. As an MIT open-source project, ASVSim fills a critical gap in the USV autonomy domain by providing the first high-fidelity simulation tool and standardized benchmark dataset. It significantly lowers the technical barrier for marine autonomous navigation research while enabling reproducible, large-scale experimentation under diverse maritime conditions.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

The transport industry has recently shown significant interest in unmanned surface vehicles (USVs), specifically for port and inland waterway transport. These systems can improve operational efficiency and safety, which is especially relevant in the European Union, where initiatives such as the Green Deal are driving a shift towards increased use of inland waterways. At the same time, a shortage of qualified personnel is accelerating the adoption of autonomous solutions. However, there is a notable lack of open-source, high-fidelity simulation frameworks and datasets for developing and evaluating such solutions. To address these challenges, we introduce AirSim For Surface Vehicles (ASVSim), an open-source simulation framework specifically designed for autonomous shipping research in inland and port environments. The framework combines simulated vessel dynamics with marine sensor simulation capabilities, including radar and camera systems and supports the generation of synthetic datasets for training computer vision models and reinforcement learning agents. Built upon Cosys-AirSim, ASVSim provides a comprehensive platform for developing autonomous navigation algorithms and generating synthetic datasets. The simulator supports research of both traditional control methods and deep learning-based approaches. Through limited experiments, we demonstrate the potential of the simulator in these research areas. ASVSim is provided as an open-source project under the MIT license, making autonomous navigation research accessible to a larger part of the ocean engineering community.

Problem

• Lack of open-source high-fidelity USV simulation frameworks
• Need for synthetic datasets for autonomous shipping research
• Shortage of tools for developing autonomous navigation algorithms

Innovation

• Open-source high-fidelity USV simulation framework
• Simulates vessel dynamics and marine sensors
• Generates synthetic datasets for AI training