VULCAN: Vision-Language-Model Enhanced Multi-Agent Cooperative Navigation for Indoor Fire-Disaster Response
For multi-agent search and rescue teams, this work identifies critical failure modes of current vision-based systems in fire scenarios, but the proposed framework is not evaluated against baselines in the fire setting, making the contribution incremental.
VULCAN introduces a VLM-enhanced multi-agent navigation framework for indoor fire-disaster response, addressing performance degradation in smoke and heat. It extends Habitat-Matterport3D with realistic fire simulations and shows that existing methods fail, highlighting the need for robust perception.
Indoor fire disasters pose severe challenges to autonomous search and rescue due to dense smoke, high temperatures, and dynamically evolving indoor environments. In such time-critical scenarios, multi-agent cooperative navigation is particularly useful, as it enables faster and broader exploration than single-agent approaches. However, existing multi-agent navigation systems are primarily vision-based and designed for benign indoor settings, leading to significant performance degradation under fire-driven dynamic conditions. In this paper, we present VULCAN, a multi-agent cooperative navigation framework based on multi-modal perception and vision-language models (VLMs), tailored for indoor fire disaster response. We extend the Habitat-Matterport3D benchmark by simulating physically realistic fire scenarios, including smoke diffusion, thermal hazards, and sensor degradation. We evaluate representative multi-agent cooperative navigation baselines under both normal and fire-driven environments. Our results reveal critical failure modes of existing methods in fire scenarios and underscore the necessity of robust perception and hazard-aware planning for reliable multi-agent search and rescue.