言語指示に基づく自律水面艇の意味的目標ナビゲーション
Language-Grounded Semantic Target Navigation for Autonomous Surface Vehicles
操縦者が言葉で指定した海上目標を、CLIPによる意味照合と強化学習制御で自律水面艇が認識・接近するフレームワークSGNavを提案し、港湾シミュレーションで高い成功率を示した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Yuqing Lin, Youngroung Kim
分類: cs.RO
原文アブストラクト
Autonomous Surface Vehicles (ASVs) are increasingly expected to operate in ports and harbour environments, where operators may specify navigation targets through language-based descriptions rather than predefined coordinates or fixed target identifiers. However, existing ASV navigation methods mainly execute predefined geometric goals or task-specific objectives and give limited attention to language-grounded target specification. This study proposes Semantically Grounded Navigation (SGNav), a framework that enables an ASV to identify and approach a maritime target from an operator-provided description. SGNav integrates text-guided semantic grounding, harbour-aware candidate filtering, CLIP-based semantic verification, grounded target control-state construction, and Proximal Policy Optimisation-based closed-loop control. It grounds the target description in onboard RGB observations, suppresses visually or semantically irrelevant distractors, and converts the selected target into a compact control-oriented representation for policy execution. Experiments in simulated port environments show that SGNav achieves success rates of $97.0\pm1.2\%$, $92.0\pm1.5\%$, and $90.0\pm1.8\%$ across three representative target-reaching tasks, with semantic target accuracy above $97\%$ and wrong-target rates below $3\%$. SGNav also maintains $97.7$--$98.7\%$ success rates across held-out port layouts. In the Task~3 ablation study, removing harbour-aware filtering or semantic consistency reduces the success rate to $40.4\pm2.6\%$ and $50.4\pm3.1\%$, respectively. These findings demonstrate the importance of semantic grounding, harbour-aware filtering, and semantic verification for reliable language-grounded ASV navigation. These results indicate that the proposed perception-to-control framework can support language-grounded target approach manoeuvres of ASV under the complex port environments.