  Autonomous Science is a field of study which aims to extend the autonomy of
exploration robots from low level functionality, such as on-board perception
and obstacle avoidance, to science autonomy, which allows scientists to specify
missions at task level. This will enable more remote and extreme environments
such as deep ocean and other planets to be studied, leading to significant
science discoveries. This paper presents an approach to extend the high level
autonomy of robots by enabling them to model and reason about scientific
knowledge on-board. We achieve this by using Bayesian networks to encode
scientific knowledge and adapting Monte Carlo Tree Search techniques to reason
about the network and plan informative sensing actions. The resulting knowledge
representation and reasoning framework is anytime, handles large state spaces
and robust to uncertainty making it highly applicable to field robotics. We
apply the approach to a Mars exploration mission in which the robot is required
to plan paths and decide when to use its sensing modalities to study a
scientific latent variable of interest. Extensive simulation results show that
our approach has significant performance benefits over alternative methods. We
also demonstrate the practicality of our approach in an analog Martian
environment where our experimental rover, Continuum, plans and executes a
science mission autonomously.
