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

Post, Mark A. and Li, Junquan and Quine, Brendan M. (2016) Planetary micro-rover operations on Mars using a Bayesian framework for inference and control. Acta Astronautica, 120. pp. 295-314. ISSN 0094-5765 (https://doi.org/10.1016/j.actaastro.2015.12.019)

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Abstract

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.

ORCID iDs

Post, Mark A. ORCID logoORCID: https://orcid.org/0000-0002-1925-7039, Li, Junquan and Quine, Brendan M.;
CORE (COnnecting REpositories)