Title:

Towards Distributed Robotic Casualty Assessment using Multimodal, Non-Contact Perception and Probabilistic Inference

Abstract:

Mass casualty incidents demand rapid and accurate triage, but the scale and acuity of injuries often overwhelm available medical personnel. To address this, we present a system that enables ground and aerial robots to assess casualties using non-contact sensors, including color and thermal cameras, millimeter wave radar, and microphones. Injury and vital sign measurements from modality-specific classifiers are fused using a probabilistic model that captures dependencies between injury states and supports distributed, asynchronous evidence accumulation. We validate the system through a series of timed, mass casualty field experiments, demonstrating reliable estimation of casualty states and robustness to noisy conditions and sensor drop out.

Bio:

Zachary Bortoff received his B.A.Sc. in Electrical Engineering from the University of Waterloo in 2022. He is currently a Ph.D. student in the Collective Dynamics and Control Lab at the University of Maryland, advised by Dr. Derek Paley. His research interests include distributed estimation, image and audio processing, and mobile robotics.