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:在本文中,我们提出了一个方法,用在线和数据驱动的方式量化机器人系统传播的不确定性。特别是在人类机器人协作中,在运行期间跟踪安全合规情况至关重要:错误地将危险情况分类为安全可能导致严重事故。根据官方条例(例如ISO标准),工业机器人应用的安全取决于关键参数,如人类和机器人之间的距离和相对速度。然而,只有测量这些参数的可靠性,才能确保安全。在文献中存在不同的风险检测和减轻风险方法,但可用于评价安全限制的计量方法在网上使用,简洁地说明一个安全或危险的情况至今尚未消失。受此驱动,我们引入了一个通用方法,用于计算任意参数的传播测量不确定性,以捕捉来自感官装置和系统环境扰动的累积不确定性。为了表明我们的方法能够产生正确的结果,我们在模拟中进行验证实验。此外,我们在两个现实世界环境中使用我们的方法,并展示如何量化关键参数的不确定性,从而便利在网上评估人类机器人协作中的安全性。