  Our goal in this paper is to plan the motion of a robot in a partitioned
environment with dynamically changing, locally sensed rewards. We assume that
arbitrary assumptions on the reward dynamics can be given. The robot aims to
accomplish a high-level temporal logic surveillance mission and to locally
optimize the collection of the rewards in the visited regions. These two
objectives often conflict and only a compromise between them can be reached. We
address this issue by taking into consideration a user-defined preference
function that captures the trade-off between the importance of collecting high
rewards and the importance of making progress towards a surveyed region. Our
solution leverages ideas from the automata-based approach to model checking. We
demonstrate the utilization and benefits of the suggested framework in an
illustrative example.
