  In this paper we present an approach to control a vehicle in a hostile
environment with static obstacles and moving adversaries. The vehicle is
required to satisfy a mission objective expressed as a temporal logic
specification over a set of properties satisfied at regions of a partitioned
environment. We model the movements of adversaries in between regions of the
environment as Poisson processes. Furthermore, we assume that the time it takes
for the vehicle to traverse in between two facets of each region is
exponentially distributed, and we obtain the rate of this exponential
distribution from a simulator of the environment. We capture the motion of the
vehicle and the vehicle updates of adversaries distributions as a Markov
Decision Process. Using tools in Probabilistic Computational Tree Logic, we
find a control strategy for the vehicle that maximizes the probability of
accomplishing the mission objective. We demonstrate our approach with
illustrative case studies.
