Technology and Field Demonstration of Robotic Search for Antarctic Meteorites

• Published in: The International Journal of Robotics Research
• Main Authors: Apostolopoulos, Dimitrios S., Wagner, Michael D., Shamah, Benjamin N., Pedersen, Liam, Shillcutt, Kimberly, Whittaker, William L.
• Format: Article in Journal/Newspaper
• Language: English
• Published: SAGE Publications 2000
• Subjects: Applied Mathematics; Artificial Intelligence; Electrical and Electronic Engineering; Mechanical Engineering; Modeling and Simulation; Software; Antarc*; Antarctic
• Online Access: http://dx.doi.org/10.1177/02783640022067940; http://journals.sagepub.com/doi/pdf/10.1177/02783640022067940

Meteorites are the only significant source of material from other planets and asteroids, and therefore are of immense scientific value. Antarctica’s frozen and pristine environment has proven to be the best place on earth to harvest meteorite specimens. The lack of melting and surface erosion keep meteorite falls visible on the ice surface in pristine condition for thousands of years. In this article, we describe the robotic technologies and field demonstration that enabled the first discovery of Antarctic meteorites by a robot. Using a novel autonomous control architecture, specialized science sensing, combined manipulation and visual servoing, and Bayesian classification, the Nomad robot found and classified five indigenous meteorites during an expedition to the remote site of Elephant Moraine in January 2000. This article first overviews Nomad’s mechatronic systems and details the control architecture that governs the robot’s autonomy and classifier that enables the autonomous interpretation of scientific data. It then focuses on the technical results achieved during field demonstrations at Elephant Moraine. Finally, the article discusses the benefits and limitations of robotic autonomy in science missions. Science autonomy is shown as a capable and expandable architecture for exploration and in situ classification. Inefficiencies in the existing implementation are explained with a focus on important lessons that outline future work.