Planetary Rover Exploration Combining Remote and In Situ Measurements for Active Spectroscopic Mapping

Maintaining high levels of productivity for planetary rovers is difficult due to limited communication and heavy reliance on ground control. There is a need for highlevel autonomy that enables rovers to take more adaptive and efficient actions based on real-time information. This paper presents an autonomous mapping and exploration approach for planetary rovers. We first describe a machine learning model that actively combines remote and rover measurements in order to map and reconstruct a scene. We focus on spectroscopic data because they are commonly used to investigate planetary surface composition. We then incorporate notions from information theory and non-myopic path planning to improve exploration productivity. Finally, we demonstrate the feasibility and successful performance of our approach via spectroscopic investigations of Cuprite, Nevada; a well-studied region of mineralogical and geological interest. We first perform a detailed analysis in simulations, and then validate those results with an actual rover in the field in Nevada.