Planetary robotic exploration driven by science hypotheses for geologic mapping

Abstract

Planetary exploration involves frequent scientific reformulation and replanning. It is limited by communication constraints and to overcome this limitation, this paper formulates the process as a collaboration in which the human scientist and the robot work together to fill in gaps in knowledge to make discoveries. It introduces the science hypothesis map as the probabilistic structure in which scientists initially describe their abstract beliefs and hypotheses, and in which the state of this belief evolves as the robot makes raw measurements. It discusses how to incorporate path planning for maximizing scientific information gain, which is efficiently computed. As proof of concept, this paper describes a geologic exploration problem where a robot uses a spectrometer to infer the geologic composition of different regions in a mining district at Cuprite, Nevada. It shows that the science hypothesis map can infer geologic units with high accuracy, and that exploration using information gain-based path planning has better performance than exploration with conventional science-blind algorithms.