面向GNSS拒止环境的自主水下无人艇设计与实验验证
Design and Experimental Validation of an Autonomous USV for Sensor Fusion-Based Navigation in GNSS-Denied Environments
AI总结：
本文提出了一种面向GNSS拒止环境的自主无人艇，通过模块化设计和传感器融合技术实现导航算法的实测验证。
摘要：
本文介绍了MARVEL无人艇的设计、开发和实验验证，该无人艇旨在实测基于传感器融合的导航算法在GNSS拒止环境中的性能。MARVEL在成本效率、便携性和适航性严格约束下开发，目标是创建一个模块化、易获取的高频率数据采集和实验学习平台。它集成了电磁测深仪、多普勒速度计、惯性传感器和实时动态GNSS定位。MARVEL通过冗余同步传感器实现实时现场验证先进导航和AI驱动算法。现场实验展示了系统在恶劣海况下的稳定性、操控性和适应性。该平台为研究人员提供了一种新颖且可扩展的方法，以获取经济实惠、开放式的工具来评估传感器融合技术在真实海洋约束下的性能。
英文摘要：
This paper presents the design, development, and experimental validation of MARVEL, an autonomous unmanned surface vehicle built for real-world testing of sensor fusion-based navigation algorithms in GNSS-denied environments. MARVEL was developed under strict constraints of cost-efficiency, portability, and seaworthiness, with the goal of creating a modular, accessible platform for high-frequency data acquisition and experimental learning. It integrates electromagnetic logs, Doppler velocity logs, inertial sensors, and real-time kinematic GNSS positioning. MARVEL enables real-time, in-situ validation of advanced navigation and AI-driven algorithms using redundant, synchronized sensors. Field experiments demonstrate the system's stability, maneuverability, and adaptability in challenging sea conditions. The platform offers a novel, scalable approach for researchers seeking affordable, open-ended tools to evaluate sensor fusion techniques under real-world maritime constraints.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
