  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.
