用语义引导让无人潜航器自主探索黑暗水下洞穴
Demonstrating CavePI: Autonomous Exploration of Underwater Caves by Semantic Guidance
- 通过轻量深度视觉模块实现环境语义理解
- 在无光照、无定位条件下成功导航复杂洞穴结构
- 适合水下考古与地质勘探场景的自主机器人研究
让自主机器人安全高效地导航、探索和绘制水下洞穴，对水资源管理、水文地质学、考古学和海洋机器人技术具有重要意义。本文展示了名为CavePI的新一代自主水下航行器（AUV）的系统设计与算法集成，采用视觉伺服框架实现语义引导的自主水下洞穴探索。系统融合了硬件与边缘AI设计，基于轻量但鲁棒的深度视觉感知模块，提供环境的丰富语义理解。随后，稳健的控制机制使CavePI能追踪语义引导，在复杂洞穴结构中自主导航。我们在自然水下洞穴与泉涌点进行了实地实验，并在基于ROS（机器人操作系统）的数字孪生环境中进一步验证。结果表明，这些集成设计在特征匮乏、无GPS、低能见度条件下仍可实现可靠导航。
原文摘要 · Abstract (English)
Enabling autonomous robots to safely and efficiently navigate, explore, and map underwater caves is of significant importance to water resource management, hydrogeology, archaeology, and marine robotics. In this work, we demonstrate the system design and algorithmic integration of a visual servoing framework for semantically guided autonomous underwater cave exploration. We present the hardware and edge-AI design considerations to deploy this framework on a novel AUV (Autonomous Underwater Vehicle) named CavePI. The guided navigation is driven by a computationally light yet robust deep visual perception module, delivering a rich semantic understanding of the environment. Subsequently, a robust control mechanism enables CavePI to track the semantic guides and navigate within complex cave structures. We evaluate the system through field experiments in natural underwater caves and spring-water sites and further validate its ROS (Robot Operating System)-based digital twin in a simulation environment. Our results highlight how these integrated design choices facilitate reliable navigation under feature-deprived, GPS-denied, and low-visibility conditions.
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