用大模型构建动态知识图谱，让机器人提前发现火灾风险并自主决策。
Robotic Fire Risk Detection based on Dynamic Knowledge Graph Reasoning: An LLM-Driven Approach with Graph Chain-of-Thought
- 通过大模型整合消防规范与救援数据构建动态知识图谱。
- 实时图像生成风险图谱，实现早期火灾预警与可解释响应。
- 适合应急机器人、智能安防领域研究者参考。
火灾是极具破坏性的灾难，但有效预防可显著降低其发生概率。一旦发生，部署应急机器人可减少对人类救援人员的危险。然而，当前灾前预警与灾时救援研究仍面临感知不全、火情认知不足和响应延迟等挑战。为提升机器人在火灾场景中的智能感知与应对规划能力，本文首先利用大语言模型（LLM）整合消防规范与机器人应急响应文档中的火灾领域知识，构建知识图谱（KG）。随后提出新框架Insights-on-Graph（IOG），融合结构化知识图谱与大型多模态模型（LMM），从实时场景图像生成感知驱动的风险图谱，实现早期火灾风险检测，并基于动态风险态势提供可解释的任务模块与机器人组件配置建议。大量仿真与真实实验表明，IOG在火灾风险检测与救援决策中具备良好适用性与实际应用价值。
原文摘要 · Abstract (English)
Fire is a highly destructive disaster, but effective prevention can significantly reduce its likelihood of occurrence. When it happens, deploying emergency robots in fire-risk scenarios can help minimize the danger to human responders. However, current research on pre-disaster warnings and disaster-time rescue still faces significant challenges due to incomplete perception, inadequate fire situational awareness, and delayed response. To enhance intelligent perception and response planning for robots in fire scenarios, we first construct a knowledge graph (KG) by leveraging large language models (LLMs) to integrate fire domain knowledge derived from fire prevention guidelines and fire rescue task information from robotic emergency response documents. We then propose a new framework called Insights-on-Graph (IOG), which integrates the structured fire information of KG and Large Multimodal Models (LMMs). The framework generates perception-driven risk graphs from real-time scene imagery to enable early fire risk detection and provide interpretable emergency responses for task module and robot component configuration based on the evolving risk situation. Extensive simulations and real-world experiments show that IOG has good applicability and practical application value in fire risk detection and rescue decision-making.
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