Sensing and data classification for robotic meteorite search

Abstract

Upcoming missions to Mars and the mon call for highly autonomous robots with capability to perform intra-site exploration, reason about their scientific finds, and perform comprehensive on-board analysis of data collected. An ideal case for testing such technologies and robot capabilities is the robotic search for Antarctic meteorites. The successful identification and classification of meteorites depends on sensing modalities and intelligent evaluation of acquired data. Data from color imagery and spectroscopic measurements are used to identify terrestrial rocks and distinguish them from meteorites. However, because of the large number of rocks and the high cost and delay of using some of the sensors, it is necessary to eliminate as many meteorite candidates as possible using cheap long range sensors, such as color cameras. More resource consuming sensor will be held in reserve for the more promising samples only. Bayes networks are used as the formalism for incrementally combing data from multiple sources in a statistically rigorous manner. Furthermore, they can be used to infer the utility of further sensor readings given currently known data. This information, along with cost estimates, in necessary for the sensing system to rationally schedule further sensor reading sand deployments. This paper address issues associated with sensor selection and implementation of an architecture for automatic identification of rocks and meteorites from a mobile robot.