Planetary micro-rover operations on Mars using a Bayesian framework for inference and control

Mark A. Post, Junquan Li, Brendan M. Quine

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
173 Downloads (Pure)


With the recent progress toward the application of commercially-available hardware to small-scale space missions, it is now becoming feasible for groups of small, efficient robots based on low-power embedded hardware to perform simple tasks on other planets in the place of large-scale, heavy and expensive robots. In this paper, we describe design and programming of the Beaver micro-rover developed for Northern Light, a Canadian initiative to send a small lander and rover to Mars to study the Martian surface and subsurface. For a small, hardware-limited rover to handle an uncertain and mostly unknown environment without constant management by human operators, we use a Bayesian network of discrete random variables as an abstraction of expert knowledge about the rover and its environment, and inference operations for control. A framework for efficient construction and inference into a Bayesian network using only the C language and fixed-point mathematics on embedded hardware has been developed for the Beaver to make intelligent decisions with minimal sensor data. We study the performance of the Beaver as it probabilistically maps a simple outdoor environment with sensor models that include uncertainty. Results indicate that the Beaver and other small and simple robotic platforms can make use of a Bayesian network to make intelligent decisions in uncertain planetary environments.
Original languageEnglish
Pages (from-to)295-314
Number of pages20
JournalActa Astronautica
Early online date25 Dec 2015
Publication statusPublished - 26 Jan 2016


  • Rover
  • decision-making
  • probalistic
  • Bayesian
  • inference
  • Mars


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