CORAL: 水中モニタリングのための階層型VLMフレームワークにおける文脈推論と局所計画
CORAL: COntextual Reasoning And Local Planning in A Hierarchical VLM Framework for Underwater Monitoring
水中環境でのオイスターリーフ監視を目的に、高次意味推論を担うVLMと低次衝突回避を担う動力学ベースプランナーを分離した階層型フレームワークCORALを提案し、探索範囲の向上と衝突削減を実現した。
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著者: Zhenqi Wu, Yuanjie Lu, Xuesu Xiao, Xiaomin Lin
分類: cs.RO
原文アブストラクト
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.
