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Improving slip prediction on Mars using thermal inertia measurements

  • Christopher Cunningham
  • Issa A. Nesnas
  • William L. Whittaker
Article
  • 3 Downloads
Part of the following topical collections:
  1. Special Issue on Robotics: Science and Systems

Abstract

Rovers operating on Mars have been delayed, diverted, and trapped by loose granular materials. Vision-based mobility prediction cannot reliably distinguish hazardous sand from safe sand based on appearance alone. Unlike surface appearance, the thermal inertia of terrain is directly correlated to the same geophysical properties that control slip. This paper presents a quantitative analysis that shows improvement in rover slip prediction when considering thermal inertia based on data from the Curiosity rover. Thermal inertia is estimated for each slip measurement in sand using both on-board and orbital instruments. Slip models are learned using a mixture of experts approach where the experts are identified using thermal inertia. Two-expert models are compared to a single-expert, vision-only model to show that slip predictions are improved by separating high-slip, low thermal inertia sand from low-slip, high thermal inertia sand. Simulated experiments are also presented to show that thermal inertia has the potential to identify sand even when it is beneath a thin layer of surface duricrust. These results support the hypothesis that the consideration of thermal inertia improves mobility estimates for rovers on Mars.

Keywords

Space robotics Traversability prediction Terramechanics Computer vision 

Notes

Acknowledgements

The authors wish to thank Mark Maimone, Sylvain Piqueux, Masahiro Ono, Jeng Yen, and Ray Arvidson for their help and advice. We thank the PDS Geosciences Node for providing image and thermal data. We also thank the Mars Science Laboratory team for providing the slip data that enabled this investigation. Portions of this research were carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. This work was supported by a NASA Space Technology Research Fellowship (Grant No. NNX13AL77H).

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Copyright information

© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply  2018

Authors and Affiliations

  1. 1.Robotics InstituteCarnegie Mellon UniversityPittsburghUSA
  2. 2.Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaUSA

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