发表机构
Nanyang Technological University（南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出SGNav框架，利用语言描述实现自主水面艇的语义目标导航，通过语义接地和港口感知过滤，在模拟港口中达到90%以上成功率，验证了方法的有效性。
AI 中文摘要
自主水面艇（ASV）日益被期望在港口和码头环境中运行，在这些环境中，操作员可能通过基于语言的描述来指定导航目标，而非预定义坐标或固定目标标识符。然而，现有的ASV导航方法主要执行预定义的几何目标或特定任务目标，对语言接地的目标指定关注有限。本研究提出语义接地导航（SGNav），一种使ASV能够根据操作员提供的描述识别并接近海上目标的框架。SGNav集成了文本引导的语义接地、港口感知的候选过滤、基于CLIP的语义验证、接地目标控制状态构建以及基于近端策略优化的闭环控制。它在机载RGB观测中接地目标描述，抑制视觉或语义上不相关的干扰物，并将选定的目标转换为紧凑的控制导向表示以供策略执行。在模拟港口环境中的实验表明，SGNav在三个代表性目标接近任务中分别实现了$97.0\pm1.2\\%$、$92.0\pm1.5\\%$和$90.0\pm1.8\\%$的成功率，语义目标准确率高于$97\\%$，错误目标率低于$3\\%$。SGNav在未见过的港口布局中保持了$97.7$--$98.7\\%$的成功率。在任务3的消融研究中，移除港口感知过滤或语义一致性分别将成功率降低至$40.4\pm2.6\\%$和$50.4\pm3.1\\%$。这些发现证明了语义接地、港口感知过滤和语义验证对于可靠的语言接地ASV导航的重要性。这些结果表明，所提出的感知到控制框架能够在复杂港口环境下支持ASV的语言接地目标接近机动。
英文摘要
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.
