Science Autonomy for Rover Subsurface Exploration of the Atacama Desert

  • David Wettergreen Carnegie Mellon University
  • Greydon Foil Carnegie Mellon University
  • Michael Furlong Carnegie Mellon University
  • David R. Thompson Jet Propulsion Laboratory, California Institute of Technology


As planetary rovers expand their capabilities, traveling longer distances, deploying complex tools, and collecting voluminous scientific data, the requirements for intelligent guidance and control also grow. This, coupled with limited bandwidth and latencies, motivates onboard autonomy that ensures the quality of the science data return. Increasing quality of the data involves better sample selection, data validation, and data reduction. Robotic studies in Mars-like desert terrain have advanced autonomy for long distance exploration and seeded technologies for planetary rover missions. In these field experiments the remote science team uses a novel control strategy that intersperses preplanned activities with autonomous decision making. The robot performs automatic data collection, interpretation, and response at multiple spatial scales. Specific capabilities include instrument calibration, visual targeting of selected features, an onboard database of collected data, and a long range path planner that guides the robot using analysis of current surface and prior satellite data. Field experiments in the Atacama Desert of Chile over the past decade demonstrate these capabilities and illustrate current challenges and future directions.

Author Biographies

David Wettergreen, Carnegie Mellon University
The Robotics Institute
Greydon Foil, Carnegie Mellon University
The Robotics Institute
Michael Furlong, Carnegie Mellon University
The Robotics Institute