Kanika Katare@Kanika0306

VesperGrid helps industrial teams detect, understand, and respond to hazards before they escalate. It brings together evidence from cameras, drones, gas sensors, wind readings, voice reports, and operator notes, then turns that information into a clear incident view with affected zones, uncertainty, source evidence, and recommended response actions. Because real industrial hazard data is sensitive and difficult to access, this project uses a fully synthetic LNG terminal scenario. A Gazebo and ROS2 simulation generates the operational environment, including CCTV views, a drone feed, a visible gas plume, gas concentration changes, and wind drift. These simulated signals are sent into a FastAPI backend through an evidence ingest pipeline, where each input is processed and linked to the incident state. The system is designed for multimodal analysis on AMD MI300X. Visual evidence is parsed with Qwen2.5-VL served through vLLM, gas and wind traces are evaluated with deterministic safety logic for stable and auditable hazard scoring, and voice reports are transcribed with faster-whisper using a configured Whisper speech-to-text model. The processed evidence then flows into VesperGrid’s main orchestration layer, which combines all inputs into one source-linked operational state. From there, VesperGrid suggests possible response actions to the human operator, explains the evidence behind each action, highlights uncertainty, and shows the likely consequences of different choices before any action is approved. The final output is shown in a React command dashboard where operators can review live feeds, inspect evidence, understand risk zones, and initiate the next response. VesperGrid does not replace the human decision-maker. It gives operators a faster, clearer, and more accountable way to act when safety depends on minutes.

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