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 localize and 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 correlations between injury states and supports distributed, asynchronous evidence accumulation. We validate the system through a series of timed mass-casualty field experiments using custom-built drones and Boston Dynamics Spot ground robots customized for robotic medical triage, demonstrating reliable estimation of casualty states and robustness to noisy conditions and sensor drop out.