A Bayesian Reasoning Framework for Robotic Systems in Autonomous Casualty Triage
For autonomous robotic systems deployed in disaster response, this work provides a method to improve decision-making under uncertainty, though it is an incremental application of Bayesian networks to a specific domain.
The paper presents a Bayesian reasoning framework for autonomous casualty triage in mass casualty incidents, achieving a three-fold improvement in physiological assessment accuracy (from 15% to 42% and 19% to 46%) and increasing overall triage accuracy from 14% to 53% compared to a vision-only baseline.
Autonomous robots deployed in mass casualty incidents (MCI) face the challenge of making critical decisions based on incomplete and noisy perceptual data. We present an autonomous robotic system for casualty assessment that fuses outputs from multiple vision-based algorithms, estimating signs of severe hemorrhage, visible trauma, or physical alertness, into a coherent triage assessment. At the core of our system is a Bayesian network, constructed from expert-defined rules, which enables probabilistic reasoning about a casualty's condition even with missing or conflicting sensory inputs. The system, evaluated during the DARPA Triage Challenge (DTC) in realistic MCI scenarios involving 11 and 9 casualties, demonstrated a nearly three-fold improvement in physiological assessment accuracy (from 15\% to 42\% and 19\% to 46\%) compared to a vision-only baseline. More importantly, overall triage accuracy increased from 14\% to 53\%, while the diagnostic coverage of the system expanded from 31\% to 95\% of cases. These results demonstrate that integrating expert-guided probabilistic reasoning with advanced vision-based sensing can significantly enhance the reliability and decision-making capabilities of autonomous systems in critical real-world applications.