  Informative path planning (IPP) is used to design paths for robotic sensor
platforms to extract the best/maximum possible information about a quantity of
interest while operating under a set of constraints, such as the dynamic
feasibility of vehicles. The key challenges of IPP are the strong coupling in
multiple layers of decisions: the selection of locations to visit, the
allocation of sensor platforms to those locations; and the processing of the
gathered information along the paths. This paper presents a systematic
procedure for IPP and environmental mapping using multiple UAV sensor
platforms. It (a) selects the best locations to observe, (b) calculates the
cost and finds the best paths for each UAV, and (c) estimates the measurement
value within a given region using the Gaussian process (GP) regression
framework. An illustrative example of RF intensity field mapping is presented
to demonstrate the validity and applicability of the proposed approach.
