  Adaptive sampling and planning in robotic environmental monitoring are
challenging when the target environmental process varies over space and time.
The underlying environmental dynamics require the planning module to integrate
future environmental changes so that action decisions made earlier do not
quickly become outdated. We propose a Monte Carlo tree search method which not
only well balances the environment exploration and exploitation in space, but
also catches up to the temporal environmental dynamics. This is achieved by
incorporating multi-objective optimization and a look-ahead model-predictive
rewarding mechanism. We show that by allowing the robot to leverage the
simulated and predicted spatiotemporal environmental process, the proposed
informative planning approach achieves a superior performance after comparing
with other baseline methods in terms of the root mean square error of the
environment model and the distance to the ground truth.
