Search papers, labs, and topics across Lattice.
This paper introduces a Bayesian network-based autonomous robotic system for casualty triage, fusing outputs from multiple vision algorithms to assess hemorrhage, trauma, and alertness. The Bayesian network, constructed using expert knowledge, enables robust probabilistic reasoning even with incomplete or conflicting sensory data. Evaluation in realistic mass casualty incident scenarios showed a significant improvement in triage accuracy (from 14% to 53%) and diagnostic coverage (from 31% to 95%) compared to a vision-only baseline.
Expert knowledge, encoded in a Bayesian network, can dramatically improve the accuracy of autonomous robotic triage systems operating in chaotic, data-scarce environments.
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.
