Decision-theoretic robot guidance for active cooperative perception

We consider the problem of sensor-aware path planning for a robot in a Networked Robot System, in particular in urban environments equipped with a network of surveillance cameras. A robot can use observations from the camera network to improve its own localization performance, but also needs to take into account the specifics of its local sensors. We model our problem in the Markov Decision Process framework, which forms a natural way to express concurrent and possibly conflicting objectives - such as reaching a goal quickly, keeping the robot localized, keeping the target in sight - each with their own priority. We show how we can successfully prioritize the different objectives in a flexible way by changing the reward function, based on the sensory needs of the system.