We consider deployment problems where a mobile robotic network must optimize
its configuration in a distributed way in order to minimize a steady-state cost
function that depends on the spatial distribution of certain probabilistic
events of interest. Three classes of problems are discussed in detail: coverage
control problems, spatial partitioning problems, and dynamic vehicle routing
problems. Moreover, we assume that the event distribution is a priori unknown,
and can only be progressively inferred from the observation of the location of
the actual event occurrences.