In this paper, we present an approach for quantifying the propagated uncertainty of robot systems in an online and data-driven manner. Especially in Human-Robot Collaboration, keeping track of the safety compliance during run time is essential: Misclassifying dangerous situations as safe might result in severe accidents. According to official regulations (eg, ISO standards), safety in industrial robot applications depends on critical parameters, such as the distance and relative velocity between humans and robots. However, safety can only be assured given a measure for the reliability of these parameters. While different risk detection and mitigation approaches exist in literature, a measure that can be used to evaluate safety limits online, and succinctly implies whether a situation is safe or dangerous, is missing to date. Motivated by this, we introduce a generalizable method for calculating the propagated measurement uncertainty of arbitrary parameters, that captures the accumulated uncertainty originating from sensory devices and environmental disturbances of the system. To show that our approach delivers correct results, we perform validation experiments in simulation. In addition, we employ our method in two real-world settings and demonstrate how quantifying the propagated uncertainty of critical parameters facilitates assessing safety online in Human-Robot Collaboration.

翻译：本文提出了一种在线且数据驱动的机器人系统传播不确定性量化方法。在人机协作场景中，运行时安全合规性的持续监测至关重要：将危险情境误判为安全可能导致严重事故。根据官方法规（如ISO标准），工业机器人应用的安全性取决于关键参数，例如人与机器人之间的距离和相对速度。然而，只有确保这些参数的可靠性度量，才能保障安全性。尽管现有文献提出了多种风险检测与缓解方法，但目前仍缺乏一种可在线评估安全界限、并简洁表征情境安全与否的度量指标。受此启发，我们提出了一种通用方法，用于计算任意参数的传播测量不确定性，其能够捕获源自系统传感设备与环境扰动的累积不确定性。为验证本方法的准确性，我们在仿真环境中进行了验证实验。此外，将所提方法应用于两个真实场景，展示了量化关键参数传播不确定性如何促进人机协作中的在线安全评估。