Skip to main content
Log in

Improving slip prediction on Mars using thermal inertia measurements

  • Published:
Autonomous Robots Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Angelova, A., Matthies, L., Helmick, D., & Perona, P. (2006). Slip prediction using visual information. In Proceedings of robotics: Science and systems, Philadelphia.

  • Angelova, A., Matthies, L., Helmick, D., & Perona, P. (2007). Learning and prediction of slip from visual information. Journal of Field Robotics, 24(3), 205–231.

    Article  Google Scholar 

  • Arvidson, R. E., Bell, J. F., Bellutta, P., Cabrol, N. A., Catalano, J. G., Cohen, J., et al. (2010). Spirit mars rover mission: Overview and selected results from the northern home plate winter haven to the side of scamander crater. Journal of Geophysical Research: Planets, 115(9), 1–19.

    Google Scholar 

  • Arvidson, R. E., Iagnemma, K. D., Maimone, M., Fraeman, A. A., Zhou, F., Heverly, M. C., et al. (2016). Mars science laboratory curiosity rover megaripple crossings up to sol 710 in gale crater. Journal of Field Robotics, 34, 495–518.

    Article  Google Scholar 

  • Bandfield, J. L., Song, E., Hayne, P. O., Brand, B. D., Ghent, R. R., Vasavada, A. R., et al. (2014). Lunar cold spots: Granular flow features and extensive insulating materials surrounding young craters. Icarus, 231, 221–231.

    Article  Google Scholar 

  • Bishop, C. M. (2006). Pattern recognition and machine learning. Information science and statistics. Berlin, Heidelberg: Springer-Verlag.

  • Brooks, C. A., & Iagnemma, K. (2012). Self-supervised terrain classification for planetary surface exploration rovers. Journal of Field Robotics, 29(3), 445–468.

    Article  Google Scholar 

  • Carrier, W. D. (2005). The four things you need to know about the geotechnical properties of lunar soil. Lakeland: Lunar Geotechnical Institute.

    Google Scholar 

  • Chhaniyara, S., Brunskill, C., Yeomans, B., Matthews, M. C., Saaj, C., Ransom, S., et al. (2012). Terrain trafficability analysis and soil mechanical property identification for planetary rovers: A survey. Journal of Terramechanics, 49(2), 115–128.

    Article  Google Scholar 

  • Christensen, P. R., Fergason, R. L., Edwards, C. S., & Hill, J. (2013). THEMIS-derived thermal inertia mosaic of mars: Product description and science results. In 44th lunar and planetary science conference (p. Abstract #2822).

  • Christensen, P. R., Jakosky, B. M., Kieffer, H. H., Malin, M. C., Mcsween, H. Y., Nealson, K., et al. (2001). The thermal emission system (THEMIS) for the Mars 2001 Odyssey mission. Space Science Reviews, 110(85–130), 2004.

    Google Scholar 

  • Cunningham, C., Nesnas, I., & Whittaker, W. L. (2015a). Terrain traversability prediction by imaging thermal transients. In IEEE conference on robotics and automation (pp. 3947–3952). Seattle.

  • Cunningham, C., Whittaker, W. L., & Nesnas, I. (2016). Detecting loose regolith in lunar craters using thermal imaging. In ASCE conference on earth and space.

  • Cunningham, C. Ono, M., Nesnas, I., Yen, J., & Whittaker, W. L. (2017). Locally-adaptive slip prediction for planetary rovers using Gaussian processes. In IEEE conference on robotics and automation.

  • Cunningham, C. Wong, U., Peterson, K. M., & Whittaker, W. L. R. (2015b). Predicting terrain traversability from thermal diffusivity. In Field and service robotics (Vol. 105, pp. 61–74), Brisbane, Australia.

  • Fergason, R. L., Christensen, P. R., Bell, J. F., Golombek, M. P., Herkenhoff, K. E., & Kieffer, H. H. (2006a). Physical properties of the mars exploration rover landing sites as inferred from mini-TES-derived thermal inertia. Journal of Geophysical Research: Planets, 111(2), E02S21.

    Google Scholar 

  • Fergason, R. L., Christensen, P. R., Golombek, M. P., & Parker, T. J. (2012). Surface properties of the mars science laboratory candidate landing sites: Characterization from orbit and predictions. Space Science Reviews, 170(1–4), 739–773.

    Article  Google Scholar 

  • Fergason, R. L., Christensen, P. R., & Kieffer, H. H. (2006b). High-resolution thermal inertia derived from the thermal emission imaging system (THEMIS): Thermal model and applications. Journal of Geophysical Research: Planets, 111(12), 1–22.

    Google Scholar 

  • Goldberg, S. B., Maimone, M. W., & Matthies, L. (2002). Stereo vision and rover navigation software for planetary exploration. IEEE Aerospace Conference Proceedings, 5, 2025–2036.

    Google Scholar 

  • Golombek, M., Grant, J., Kipp, D., Vasavada, A., Kirk, R., Fergason, R., et al. (2012). Selection of the mars science laboratory landing site. Space Science Reviews, 170(1–4), 641–737.

    Article  Google Scholar 

  • Grotzinger, J. P., Crisp, J., Vasavada, A. R., Anderson, R. C., Baker, C. J., Barry, R., et al. (2012). Mars science laboratory mission and science investigation. Space Science Reviews, 170(1–4), 5–56.

    Article  Google Scholar 

  • Halatci, I., Brooks, C. A., & Iagnemma, K. (2007). Terrain classification and classifier fusion for planetary exploration rovers. In IEEE aerospace conference proceedings (pp. 1–11). IEEE.

  • Hamilton, V. E., Vasavada, A. R., Sebastiiann, E., Juarez, M. D. L. T., Ramos, M., Armiens, C., et al. (2014). Observations and preliminary science results from the first 100 sols of MSL rover environmental monitoring station ground temperature sensor measurements at gale crater. Journal of Geophysical Research: Planets, 119(4), 745–770.

    Google Scholar 

  • Heverly, M., Matthews, J., Lin, J., Fuller, D., Maimone, M., Baesaidecki, J., et al. (2013). Traverse performance characterization for the mars science laboratory rover. Journal of Field Robotics, 30(6), 835–846.

    Article  Google Scholar 

  • Ho, K., Peynot, T., & Sukkarieh, S. (2016). Nonparametric traversability estimation in partially occluded and deformable terrain. Journal of Field Robotics, 33(8), 1131–1158.

    Article  Google Scholar 

  • Igwe, O., Sassa, K., & Wang, F. (2007). The influence of grading on the shear strength of loose sands in stress-controlled ring shear tests. Landslides, 4(1), 43–51.

    Article  Google Scholar 

  • Jakosky, B. M. (1986). On the thermal properties of martian fines. Icarus, 66(1), 117–124.

    Article  Google Scholar 

  • Kieffer, H. H. (2013). Thermal model for analysis of mars infrared mapping. Journal of Geophysical Research: Planets, 118(3), 451–470.

    Google Scholar 

  • Lade, P. V., & Overton, D. D. (1989). Cementation effects in frictional materials. Journal of Geotechnical Engineering, 115(10), 1373–1387.

    Article  Google Scholar 

  • Lemmon, M. T. (2014). The Mars science laboratory optical depth record. In LPI contributions (pp. 1–2).

  • Lemmon, M. T., Wolff, M. J., Bell, J. F., Smith, M. D., Cantor, B. A., & Smith, P. H. (2015). Dust aerosol, clouds, and the atmospheric optical depth record over 5 mars years of the mars exploration rover mission. Icarus, 251, 96–111.

    Article  Google Scholar 

  • Maimone, M. (2016). No a Martian vision: Impact of JPL robotics vision and mobility research on the mars rovers. In JPL robotics section senior lecture series.

  • Maimone, M., Cheng, Y., & Matthies, L. (2007). Two years of visual odometry on the mars exploration rovers. Journal of Field Robotics, 24(3), 169–186.

    Article  Google Scholar 

  • Martínez, G. M., Rennõ, N., Fischer, E., Borlina, C. S., Hallet, B., De La Torre Juárez, M., et al. (2014). Surface energy budget and thermal inertia at gale crater: Calculations from ground-based measurements. Journal of Geophysical Research: Planets, 119(8), 1822–1838.

    Google Scholar 

  • Mellon, M. T., Jakosky, B. M., Kieffer, H. H., & Christensen, P. R. (2000). High-resolution thermal inertia mapping from the mars global surveyor thermal emission spectrometer. Icarus, 148(2), 437–455.

    Article  Google Scholar 

  • Morgan, J. K. (1999). Numerical simulations of granular shear zones using the distinct element method 2. Effects of particle size distribution and interparticle friction on mechanical behavior. Journal of Geophysical Research, 104(B2), 2721–2732.

    Article  Google Scholar 

  • Otsu, K., Ono, M., Fuchs, T. J., Baldwin, I., & Kubota, T. (2016). Autonomous terrain classification with co- and self-training approach. IEEE Robotics and Automation Letters, 1(2), 814–819.

    Article  Google Scholar 

  • Parsons, A. J., & Abrahams, A. D. (2009). Geomorphology of desert environments. ISBN 9781402057182.

  • Perko, H. A., Nelson, J. D., & Green, J. R. (2006). Mars soil mechanical properties and suitability of mars soil simulants. Journal of Aerospace Engineering, 19(July), 169–176.

    Article  Google Scholar 

  • Piqueux, S., & Christensen, P. R. (2009a). A model of thermal conductivity for planetary soils: 2. Theory for unconsolidated soils. Journal of Geophysical Research: Planets, 114(9), 1–20.

    Google Scholar 

  • Piqueux, S., & Christensen, P. R. (2009b). A model of thermal conductivity for planetary soils: 2. Theory for cemented soils. Journal of Geophysical Research: Planets, 114(9), 1–20.

    Google Scholar 

  • Presley, M. A., & Christensen, P. R. (2010). Thermal conductivity measurements of particulate materials: 5. Effect of bulk density and particle shape. Journal of Geophysical Research: Planets, 115(E7), 1–13.

  • Presley, M. A., & Christensen, P. R. (1997). Thermal conductivity measurements of particulate materials 2. Results. Journal of Geophysical Research: Planets, 102(E3), 6551–6566.

    Article  Google Scholar 

  • Price, J. C. (1977). Thermal inertia mapping: A new view of the earth. Journal of Geophysical Research, 82(18), 2582.

    Article  Google Scholar 

  • Putzig, N. E. (2006). Thermal inertia and surface heterogeneity on mars. Ph.D. thesis, University of Colorado.

  • Putzig, N. E., & Mellon, M. T. (2007). Apparent thermal inertia and the surface heterogeneity of mars. Icarus, 191(1), 68–94.

    Article  Google Scholar 

  • Putzig, N. E., Mellon, M. T., Jakosky, B. M., Pelkey, S. M., Martínez-Alonso, S., Hynek, B. M., et al. (2004). Mars thermal inertia from THEMIS data. Lunar and Planetary Science Conference, 35, 1863.

    Google Scholar 

  • Rasmussen, C. E., & Ghahramani, Z. (2002). Infinite mixtures of gaussian process experts. Advances in Neural Information Processing Systems, 2, 881–888.

    Article  Google Scholar 

  • Rodriguez-Manfredi, J. A., Gomez-Gomez, F., Gomez-Elvira, J., Navarro, S., Prieto-Ballesteros, O., Sebastian, E., et al. (2017). Atmospheric science with the mars 2020 rover. In The sixth international workshop on the mars atmosphere (p. 4408).

  • Rothrock, B., Papon, J., Kennedy, R., Ono, M., Heverly, M., & Cunningham, C. (2016). SPOC: Deep learning-based terrain classification for mars rover missions. In AIAA: Space.

  • Sebastián, E., Armiens, C., Gómez-Elvira, J., Zorzano, M. P., Martinez-Frias, J., Esteban, B., et al. (2010). The rover environmental monitoring station ground temperature sensor: A pyrometer for measuring ground temperature on mars. Sensors, 10(10), 9211–9231.

    Article  Google Scholar 

  • Squyres, S. W., Arvidson, R. E., Bollen, D., Bell, J. F., Bruckner, J., Cabrol, N. A., et al. (2006). Overview of the opportunity mars exploration rover mission to meridiani planum: Eagle crater to purgatory ripple. Journal of Geophysical Research: Planets, 111(12), 1–19.

    Google Scholar 

  • Vasavada, A. R., Bandfield, J. L., Greenhagen, B. T., Hayne, P. O., Siegler, M. A., Williams, J. P., et al. (2012). Lunar equatorial surface temperatures and regolith properties from the diviner lunar radiometer experiment. Journal of Geophysical Research: Planets, 117(4), 1–12.

    Google Scholar 

  • Wang, L. C., Long, W., & Gao, S. J. (2014). Effect of moisture content, void ratio and compacted sand content on the shear strength of remolded unsaturated clay. Electronic Journal of Geotechnical Engineering, 19(Q), 4413–4426.

    Google Scholar 

  • Wissa, A. E. Z., Ladd, C. C., & Lambe, T. W. (1964). Effective stress strength parameters of stabilized soils. In MIT Department of Civil Engineering.

  • Wong, J. Y. (2012). Predicting the performances of rigid rover wheels on extraterrestrial surfaces based on test results obtained on earth. Journal of Terramechanics, 49(1), 49–61.

    Article  Google Scholar 

  • Yuksel, S. E., Wilson, J. N., & Gader, P. D. (2012). Twenty years of mixture of experts. IEEE Transactions on Neural Networks and Learning Systems, 23(8), 1177–1193.

    Article  Google Scholar 

Download references

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).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Christopher Cunningham.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This is one of several papers published in Autonomous Robots comprising the “Special Issue on Robotics Science and Systems”.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cunningham, C., Nesnas, I.A. & Whittaker, W.L. Improving slip prediction on Mars using thermal inertia measurements. Auton Robot 43, 503–521 (2019). https://doi.org/10.1007/s10514-018-9796-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10514-018-9796-4

Keywords

Navigation