CAVE-NAV: VLM-Based Autonomous 3D Navigation in Underwater Cave Environments

2026-08-27

AI Summary

为解决水下洞穴环境中自主导航的问题，提出了一种基于视觉-语言模型（VLM）和链式思维推理的框架，利用多模态输入信息推断可航行方向。

Abstract

Autonomous navigation in underwater cave environments is essential for search-and-rescue operations, scientific exploration, and emergency egress. Traditional navigation systems commonly depend on dense visual features for localization and mapping. In underwater caves, however, visual degradation can undermine feature-based localization, sonar-based mapping may yield overly conservative obstacle representations, and communication constraints preclude real-time human guidance. To address these limitations, we propose an autonomous underwater cave navigation framework that leverages a vision-language model (VLM) with Chain-of-Thought (CoT) reasoning to infer navigable directions from environmental cues, including light intensity gradients, passage morphology, and geometric complexity, captured through multimodal inputs comprising RGB imagery, depth maps, and sonar-based vertical-clearance measurements, thereby supporting safe 3D navigation through confined cave passages. High-fidelity simulations across multiple cave topologies demonstrate that the proposed framework completes all evaluated end-to-end traversals without collisions while maintaining safe clearance from cave boundaries.

Problem

• autonomous navigation
• underwater cave environments
• visual degradation
• feature-based localization
• communication constraints

Innovation

• vision-language model
• Chain-of-Thought reasoning
• multimodal inputs