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Adaptive sampling and planning in robotic environmental monitoring arechallenging when the target environmental process varies over space and time.The underlying environmental dynamics require the planning module to integratefuture environmental changes so that action decisions made earlier do notquickly become outdated. We propose a Monte Carlo tree search method which notonly well balances the environment exploration and exploitation in space, butalso catches up to the temporal environmental dynamics. This is achieved byincorporating multi-objective optimization and a look-ahead model-predictiverewarding mechanism. We show that by allowing the robot to leverage thesimulated and predicted spatiotemporal environmental process, the proposedinformative planning approach achieves a superior performance after comparingwith other baseline methods in terms of the root mean square error of theenvironment model and the distance to the ground truth.

Multi-Objective and Model-Predictive Tree Search for Spatiotemporal Informative Planning