  This paper considers safe robot mission planning in uncertain dynamical
environments. This problem arises in applications such as surveillance,
emergency rescue, and autonomous driving. It is a challenging problem due to
modeling and integrating dynamical uncertainties into a safe planning
framework, and finding a solution in a computationally tractable way. In this
work, we first develop a probabilistic model for dynamical uncertainties. Then,
we provide a framework to generate a path that maximizes safety for complex
missions by incorporating the uncertainty model. We also devise a Monte Carlo
method to obtain a safe path efficiently. Finally, we evaluate the performance
of our approach and compare it to potential alternatives in several case
studies.
