Robotic Fire Risk Detection based on Dynamic Knowledge Graph Reasoning: An LLM-Driven Approach with Graph Chain-of-Thought
This work addresses fire risk detection and rescue planning for emergency robots, but it appears incremental as it builds on existing knowledge graph and multimodal model techniques.
The paper tackled the problem of incomplete perception and delayed response in robotic fire risk detection and rescue by constructing a knowledge graph using LLMs and proposing the Insights-on-Graph framework, which integrates multimodal data to enable early detection and interpretable emergency responses, showing good applicability in simulations and real-world experiments.
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.