The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

Soon robotic explorers will be able to produce more scientific data than can be transmitted or interpreted efficiently. We present a method for characterizing geology during rover traverse using autonomous data analysis techniques. The strategy detects discrete geologic features in images; distributions of these features constitute a signature that correlates with the geology of each site. These numerical profiles reveal subtle trends and boundaries in geologic units that facilitate targeted sample selection and efficient data analysis. We demonstrate the system's use on field data collected during a field expedition to the Atacama Desert of Chile.

Keywords

Autonomous Science, Robotic Exploration, Autonomous Geology

Notes

Sponsor: NASA

Grant ID: NNG0-4GB66G, NAG5-12890

Associated Center(s) / Consortia: Field Robotics Center

Associated Project(s): Science Autonomy and Life in the Atacama

Number of pages: 8

Text Reference

David R. Thompson, Trey Smith, and David Wettergreen, "Data Mining During Rover Traverse: From Images to Geologic Signatures," 8th International Symposium on Artificial Intelligence, Robotics and Automation in Space, September, 2005.

BibTeX Reference

@inproceedings{Thompson_2005_5114,
   author = "David R Thompson and Trey Smith and David Wettergreen",
   title = "Data Mining During Rover Traverse: From Images to Geologic Signatures",
   booktitle = "8th International Symposium on Artificial Intelligence, Robotics and Automation in Space",
   month = "September",
   year = "2005",
}

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