Oyster reefs are critical ecosystem species that sustain biodiversity, filter water, and protect coastlines, yet they continue to decline globally. Restoring these ecosystems requires regular underwater monitoring to assess reef health, a task that remains costly, hazardous, and limited when performed by human divers. Autonomous underwater vehicles (AUVs) offer a promising alternative, but existing AUVs rely on geometry-based navigation that cannot interpret scene semantics. Recent vision-language models (VLMs) enable semantic reasoning for intelligent exploration, but existing VLM-driven systems adopt an end-to-end paradigm, introducing three key limitations. First, these systems require the VLM to generate every navigation decision, forcing frequent waits for inference. Second, VLMs cannot model robot dynamics, causing collisions in cluttered environments. Third, limited self-correction allows small deviations to accumulate into large path errors. To address these limitations, we propose CORAL, a framework that decouples high-level semantic reasoning from low-level reactive control. The VLM provides high-level exploration guidance by selecting waypoints, while a dynamics-based planner handles low-level collision-free execution. A geometric verification module validates waypoints and triggers replanning when needed. Compared with the previous state-of-the-art, CORAL improves coverage by 14.28% percentage points, or 17.85% relatively, reduces collisions by 100%, and requires 57% fewer VLM calls.

翻译：牡蛎礁是维持生物多样性、净化水质和保护海岸线的关键生态系统物种，然而全球范围内其数量仍在持续减少。恢复这些生态系统需要定期进行水下监测以评估礁体健康状况，而由人类潜水员执行该任务成本高昂、危险且受限。自主水下航行器（AUV）提供了有前景的替代方案，但现有AUV依赖基于几何的导航方法，无法理解场景语义。近期发展的视觉语言模型（VLM）虽能实现智能探索所需的语义推理，但现有VLM驱动系统采用端到端范式，存在三个关键局限：首先，这些系统要求VLM生成所有导航决策，导致频繁等待推理过程；其次，VLM无法建模机器人动力学，在复杂环境中易引发碰撞；第三，有限的自我校正能力使微小偏差累积成显著路径误差。为解决这些局限，我们提出CORAL框架，将高层语义推理与低层反应控制解耦。VLM通过选择航点提供高层探索指导，而基于动力学的规划器负责执行无碰撞的低层运动。几何验证模块对航点进行校验，并在需要时触发重新规划。与先前最优方法相比，CORAL将监测覆盖率绝对提升14.28个百分点（相对提升17.85%），碰撞率降低100%，且VLM调用次数减少57%。