Coordinating multi-robot systems (MRS) to search in unknown environments is particularly challenging for tasks that require semantic reasoning beyond geometric exploration. Classical coordination strategies rely on frontier coverage or information gain and cannot incorporate high-level task intent, such as searching for objects associated with specific room types. We propose \textit{Semantic Area Graph Reasoning} (SAGR), a hierarchical framework that enables Large Language Models (LLMs) to coordinate multi-robot exploration and semantic search through a structured semantic-topological abstraction of the environment. SAGR incrementally constructs a semantic area graph from a semantic occupancy map, encoding room instances, connectivity, frontier availability, and robot states into a compact task-relevant representation for LLM reasoning. The LLM performs high-level semantic room assignment based on spatial structure and task context, while deterministic frontier planning and local navigation handle geometric execution within assigned rooms. Experiments on the Habitat-Matterport3D dataset across 100 scenarios show that SAGR remains competitive with state-of-the-art exploration methods while consistently improving semantic target search efficiency, with up to 18.8\% in large environments. These results highlight the value of structured semantic abstractions as an effective interface between LLM-based reasoning and multi-robot coordination in complex indoor environments.

翻译：协调多机器人系统在未知环境中搜索，对于需要超越几何探索的语义推理任务尤为具有挑战性。经典协调策略依赖前沿覆盖或信息增益，无法融入高层次任务意图（例如搜索与特定房间类型关联的物体）。我们提出语义区域图推理（SAGR），这是一种分层框架，通过环境的结构化语义-拓扑抽象，使大语言模型（LLM）能够协调多机器人探索与语义搜索。SAGR从语义占据图中增量式构建语义区域图，将房间实例、连通性、前沿可用性及机器人状态编码为紧凑的任务相关表示，供LLM推理。LLM基于空间结构和任务上下文执行高层语义房间分配，而确定性前沿规划与局部导航负责处理分配房间内的几何执行。在Habitat-Matterport3D数据集上的100个场景实验表明，SAGR在与最先进探索方法保持竞争力同时，持续提升语义目标搜索效率（在大型环境中最高提升18.8%）。这些结果凸显了结构化语义抽象作为LLM推理与多机器人协调之间有效接口在复杂室内环境中的价值。