Active Sampling for Planetary Rover Exploration

Abstract

Planetary Robotics research has expanded beyond simply developing robust navigation strategies for rovers to providing them with the capability of performing intelligent actions so as to develop a better interpretation and understanding of the environment. This will become essential in the future, when rovers explore regions far away from Earth, at distances that would significantly throttle communication with human operators. This research focuses on two problems of interest: Spatio-Spectral Exploration and Active Spectral Reconstruction. For both of these problems we develop a Markov Decision Process framework reliant on using remote sensing measurements that circumvents the partial observability present in these problems. Further, we propose a Monte-Carlo Tree Search based approach for efficiently sampling relevant locations so as to solve these problems. We demonstrate our approach in simulation as well as testing it with the rover Zo{"e} at a Mars-analogous terrain located at Cuprite, NV and highlight its advantages compared to traditional planning strategies.