A Multi-Robot Platform for Robotic Triage Combining Onboard Sensing and Foundation Models
Hughes, Jason, Hussing, Marcel, Zhang, Edward, Kannapiran, Shenbagaraj, Caswell, Joshua, Chaney, Kenneth, Deng, Ruichen, Feehery, Michaela, Kratimenos, Agelos, Li, Yi Fan, Major, Britny, Sanchez, Ethan, Shrote, Sumukh, Wang, Youkang, Wang, Jeremy, Zein, Daudi, Zhang, Luying, Zhang, Ruijun, Zhou, Alex, Zhouga, Tenzi, Cannon, Jeremy, Qasim, Zaffir, Yelon, Jay, Cladera, Fernando, Daniilidis, Kostas, Taylor, Camillo J., Eaton, Eric
–arXiv.org Artificial Intelligence
Abstract-- This report presents a heterogeneous robotic system designed for remote primary triage in mass-casualty incidents (MCIs). The system employs a coordinated air-ground team of unmanned aerial vehicles (UA Vs) and unmanned ground vehicles (UGVs) to locate victims, assess their injuries, and prioritize medical assistance without risking the lives of first responders. The UA V identify and provide overhead views of casualties, while UGVs equipped with specialized sensors measure vital signs and detect and localize physical injuries. Unlike previous work that focused on exploration or limited medical evaluation, this system addresses the complete triage process: victim localization, vital sign measurement, injury severity classification, mental status assessment, and data consolidation for first responders. Developed as part of the DARPA Triage Challenge, this approach demonstrates how multi-robot systems can augment human capabilities in disaster response scenarios to maximize lives saved. I. INTRODUCTION Robotics has long sought to augment human capabilities in hazardous scenarios. Mass-casualty incidents (MCIs), such as those resulting from natural disasters, bombings, plane crashes, or industrial chemical spills, present an opportunity for robotic systems to assist first responders. The critical first step of providing medical assistance during MCIs is primary triage: the initial process of locating victims at the site of the MCI and assessing the severity of their injuries to prioritize treatment, which is essential to optimizing survival outcomes. Traditionally, primary triage relies on human responders who may face significant risk and information overload [1], underscoring the potential for automated systems to mitigate these challenges. While prior efforts have explored the use of air-ground robotic teams for search and exploration in disaster zones [2]-[5], few systems have focused specifically on rapid triage. Existing approaches typically solve parts of the problem in isolation without integrating comprehensive triage functions. For example, air-ground teams have also been developed to find and localize objects of interest [3], [6] Authors are with the GRASP Lab, School of Engineering and Applied Sciences, University of Pennsylvania. Authors are with the Perelman School of Medicine, University of Pennsylvania. This work was supported by the DARP A Triage Challenge under grant #HR001123S0011.
arXiv.org Artificial Intelligence
Dec-10-2025
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- Tokyo (0.04)
- Kansai > Hyogo Prefecture
- Honshū
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