Journal of Field Robotics - Special Issue on Space Robotics, Part III
Autonomous science for an ExoMars Rover–like mission
Journal of Field Robotics - Special Issue on Space Robotics, Part II
Modeling and Reasoning with Bayesian Networks
Modeling and Reasoning with Bayesian Networks
Theta*: any-angle path planning on grids
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Incremental Phi*: incremental any-angle path planning on grids
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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Autonomous science augments the capabilities of planetary rovers by shifting the identification and selection of science targets from remote operators to the rover itself. This shift frees the rover from wasteful idle time and allows for more selective data collection. This paper presents an approach to autonomous science that is comprised of three components: a Bayesian network that uses image data to identify features; an evaluation algorithm that selects the best features; and, a path-planning algorithm that guides the rover to the most scientifically valuable features. Within this framework, the effectiveness of pairing a larger prime rover with a smaller scout rover to improve autonomous science is investigated. Laboratory-based experiments were used to validate the effectiveness of the Bayesian network for feature identification and the scoring algorithm that has been developed for feature evaluation. Simulations were used to compare the traditional use of a solo prime rover to that of also employing a scout. The results presented here indicate that the inclusion of a scout rover can allow the prime rover to avoid pitfalls or routes with low scientific value.