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.

翻译：大规模伤亡事件（MCI）中部署的自主机器人面临着基于不完整且含噪声的感知数据做出关键决策的挑战。我们提出了一种用于伤员评估的自主机器人系统，该系统能够融合多个基于视觉算法的输出（包括严重出血迹象、可见创伤或身体警觉性评估）形成一致的检伤分类评估。该系统的核心是一个基于专家定义规则构建的贝叶斯网络，即使面对缺失或矛盾的感官输入，也能对伤员状况进行概率推理。该系统在DARPA检伤分类挑战赛（DTC）中针对包含11名和9名伤员的实际MCI场景进行了评估，结果显示其生理评估准确率相比仅依赖视觉的基线方法提升了近三倍（从15%提升至42%，从19%提升至46%）。更重要的是，整体检伤分类准确率从14%提升至53%，同时系统的诊断覆盖率从31%扩展至95%。这些结果表明，将专家引导的概率推理与先进的视觉感知相结合，能够显著提升自主系统在关键现实应用中的可靠性和决策能力。