Real-time fault detection and situational awareness for rovers: Report on the mars technology program task.

R. Dearden, F. Huttner, R. Simmons, V. Verma, T. Willeke, and S. Thrun.

An increased level of autonomy is critical for meeting many of the goals of advanced planetary rover missions such as NASA's 2009 Mars Science Lab. One important component of this is state estimation, and in particular fault detection on-board the rover. In this paper we describe the results of a project funded by the Mars Technology Program at NASA, aimed at developing algorithms to meet this requirement. We describe a number of particle filtering-based algorithms for state estimation which we have demonstrated successfully on diagnosis problems including the K-9 rover at NASA Ames Research Center and the Hyperion rover at CMU. Because of the close interaction between a rover and its environment, traditional discrete approaches to diagnosis are impractical for this domain. Therefore we model rover subsystems as hybrid discrete/continuous systems. There are three major challenges to make particle filters work in this domain. The first is that fault states typically have a very low probability of occurring, so there is a risk that no samples will enter fault states. The second issue is coping with the high-dimensional continuous state spaces of the hybrid system models, and the third is the severely constrained computational power available on the rover. This means that very few samples can be used if we wish to track the system state in real time. We describe a number of approaches to rover diagnosis specifically designed to address these challenges.

The full paper is available in PDF and gzipped Postscript

  AUTHOR	= {Dearden, R. and Huttner, F. and Simmons, R. and Verma, V. and Thrun, S. and Willeke, T.},
  TITLE		= {Real-Time Fault Detection and Situational Awareness for Rovers: Report on the Mars Technology Program Task},
  YEAR		= {2004},
  MONTH		= {March},
  BOOKTITLE	= {Proceedings of IEEE Aerospace Conference},
  ADDRESS	= {Big Sky, MY}