  This paper investigates the problem of informative path planning for a mobile
robotic sensor network in spatially temporally distributed mapping. The robots
are able to gather noisy measurements from an area of interest during their
movements to build a Gaussian Process (GP) model of a spatio-temporal field.
The model is then utilized to predict the spatio-temporal phenomenon at
different points of interest. To spatially and temporally navigate the group of
robots so that they can optimally acquire maximal information gains while their
connectivity is preserved, we propose a novel multistep prediction informative
path planning optimization strategy employing our newly defined local cost
functions. By using the dual decomposition method, it is feasible and practical
to effectively solve the optimization problem in a distributed manner. The
proposed method was validated through synthetic experiments utilizing
real-world data sets.
