Active robotic sensing is a large field aimed at providing robotic systems with tools and methods for decision making under uncertainty, e.g. in a changing environment and with a lack of sufficient information. Active sensing (AS) incorporates the following aspects: (i) where to position sensors, (ii) how to make decisions for subsequent actions in order to extract maximum information from the sensor data and minimize costs such as travel time and energy. We concentrate on the second aspect: “where should the robot move at the next time step?” and present AS in a probabilistic decision theoretic framework. The AS problem is formulated as a constrained optimization with a multi-objective criterion combining an information gain and a cost term with respect to generated actions. Solutions for AS of autonomous mobile robots are given, illustrating the framework.