  We present an online and data-driven uncertainty quantification method to
enable the development of safe human-robot collaboration applications. Safety
and risk assessment of systems are strongly correlated with the accuracy of
measurements: Distinctive parameters are often not directly accessible via
known models and must therefore be measured. However, measurements generally
suffer from uncertainties due to the limited performance of sensors, even
unknown environmental disturbances, or humans. In this work, we quantify these
measurement uncertainties by making use of conservation measures which are
quantitative, system specific properties that are constant over time, space, or
other state space dimensions. The key idea of our method lies in the immediate
data evaluation of incoming data during run-time referring to conservation
equations. In particular, we estimate violations of a-priori known, domain
specific conservation properties and consider them as the consequence of
measurement uncertainties. We validate our method on a use case in the context
of human-robot collaboration, thereby highlighting the importance of our
contribution for the successful development of safe robot systems under
real-world conditions, e.g., in industrial environments. In addition, we show
how obtained uncertainty values can be directly mapped on arbitrary safety
limits (e.g, ISO 13849) which allows to monitor the compliance with safety
standards during run-time.

Robotics