Autonomous Robots

, Volume 14, Issue 2–3, pp 199–208 | Cite as

Mars Rover Autonomous Navigation

  • M. Maurette

Abstract

Autonomous navigation of a rover on Mars surface can improve very significantly the daily traverse, particularly when driving away from the lander, into unknown areas. The autonomous navigation process developed at CNES is based on stereo cameras perception, used to build a model of the environment and generate trajectories. Multiple perception merging with propagation of the locomotion and localization errors have been implemented. The algorithms developed for Mars exploration programs, the vision hardware, the validation tools, experimental platforms and results of evaluation are presented. Portability and the evaluation of computing resources for implementation on a Mars rover are also addressed. The results show that the implementation of autonomy requires only a very small amount of energy and computing time and that the rover capabilities are fully used, allowing a much longer daily traverse than what is enabled by purely ground-planned strategies.

rover stereovision autonomous navigation planetary exploration 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Balazs, A. et al. 1998. Locomotion system of the IARES demonstrator for planetary exploration. Space Technology, 17(3/4).Google Scholar
  2. Ballard, P. and Vacherand, F. 1994. The Manhattan method: A fast cartesian elevation map reconstruction from range data. In IEEE International Conference on Robotics and Automation, San Diego CA.Google Scholar
  3. Delpech, M., Proy, C., and Rastel, L. 1997. High rate autonomous navigation for a non stop traversing rover. In ISAIRAS 97.Google Scholar
  4. Hait, A., Simeon, T., and Taix, M. 1997. Robust motion planning for rough terrain navigation. In IEEE/RSJ International Conference on Robotics and Automation, Albuquerque.Google Scholar
  5. Kweon, I.S. and Kanade, T. 1992. High resolution terrain map from multiple sensor data. In IEEE Transaction on Pattern Analysis and Machine Intelligence.Google Scholar
  6. Lindeman, R., Reid, L., and Voorhees, C. 1999. Mobility subsystem for the exploration technology rover. In 33rd Aerospace Mechanisms Symposium, Pasadena.Google Scholar
  7. Mallet, A., Lacroix, S., and Gallo, L. 2000. Position estimation in outdoor environments using pixel tracking and stereovision. In IEEE International Conference on Robotics and Automation, San Francisco.Google Scholar
  8. Mathies, L., Kelly, A., and Litwin, T. 1995. Obstacle detection for unmanned ground vehicles: A progress report. International Symposium of Robotics Research, Munich.Google Scholar
  9. Matijevic, J. and Shirley, D. 1996. The mission and operation of the Mars Pathfinder microrover. In IFAC 13th Triennal World Congress, San Francisco USA.Google Scholar
  10. Maurette, M. and Baumgartner, E.T. 2000. Autonomous navigation ability: FIDO tests results. In 6th ESA Workshop ASTRA, ESTEC NL.Google Scholar
  11. Maurette, M., Boissier, L., Delpech, M., Proy, C., and Quere, C. 1997. Autonomy and remote control experiment for lunar rover mission. Control Eng. Practice 5(6).Google Scholar
  12. Runavot, J. and Delail, M. 1992. A CNES facility to simulate CNES landscape. In IAF Congress.Google Scholar

Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • M. Maurette
    • 1
  1. 1.Toulouse, CedexFrance

Personalised recommendations