Non-myopic Planetary Exploration Combining In Situ and Remote Measurements

Remote sensing measurements can provide crucial information about the material properties of a planetary surface but their application is limited by their spatial resolution, typically tens of meters per pixel, when constituent materials are mixed at much finer scale. Consequently the orbital observations must be validated with in situ measurements from a spectrometer on the ground. In planetary exploration this means that a rover must visit selected locations that jointly improve a model of the environment and satisfy mobility and sampling constraints. Conventional planning methods used in this situation follow sub-optimal greedy strategies that are not scalable to large areas. We show how the problem can be effectively defined in a Markov Decision Process framework and propose a planning algorithm based on Monte Carlo Tree Search, which is efficient but devoid of these drawbacks thereby providing superior performance. We evaluate our approach using hyperspectral imagery of a well-studied geologic site in Cuprite, Nevada.