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Radiological emergencies present unique challenges due to the urgency of protective actions, hazardous environments, and the need for rapid situational assessment. Unmanned Aerial Systems (UASs), enhanced with artificial intelligence (AI), offer transformative capabilities in this domain, integrating radiation mapping, search-and-rescue optimization, and ecosystem monitoring to strengthen nuclear safety and emergency response. This article documents a framework perspective for AI-enabled autonomous UAS systems, combining infrared, visual, and Compton camera imaging with real-time machine learning for precise hazard detection and environmental assessment based on available technologies. Operational scenarios co-designed with the help of direct involved parties demonstrate UAS applications in nuclear disaster management, infrastructure security, and post-incident ecological monitoring, highlighting their ability to reduce human exposure, enhance situational awareness, and support informed decision-making. Future research directions include cross-border collaboration to improve preparedness and response for nuclear or radiological incidents.

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From Search-and-Rescue to Nuclear Scenarios: AI-Enabled UAS for Disaster Management, Security, and Ecosystem Monitoring

  • 会议论文

  • 发表日期：

    2026年

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  • Alexandru Nistorescu,
  • Mihaela Marin,
  • Kevin Dominey,
  • Adrian Cătălin Dinculescu,
  • Cosmin Dugan,
  • Cristian Vizitiu,
  • Petre Min

摘要

Radiological emergencies present unique challenges due to the urgency of protective actions, hazardous environments, and the need for rapid situational assessment. Unmanned Aerial Systems (UASs), enhanced with artificial intelligence (AI), offer transformative capabilities in this domain, integrating radiation mapping, search-and-rescue optimization, and ecosystem monitoring to strengthen nuclear safety and emergency response. This article documents a framework perspective for AI-enabled autonomous UAS systems, combining infrared, visual, and Compton camera imaging with real-time machine learning for precise hazard detection and environmental assessment based on available technologies. Operational scenarios co-designed with the help of direct involved parties demonstrate UAS applications in nuclear disaster management, infrastructure security, and post-incident ecological monitoring, highlighting their ability to reduce human exposure, enhance situational awareness, and support informed decision-making. Future research directions include cross-border collaboration to improve preparedness and response for nuclear or radiological incidents.