  Robotic science missions in remote environments, such as deep ocean and outer
space, can involve studying phenomena that cannot directly be observed using
on-board sensors but must be deduced by combining measurements of correlated
variables with domain knowledge. Traditionally, in such missions, robots
passively gather data along prescribed paths, while inference, path planning,
and other high level decision making is largely performed by a supervisory
science team. However, communication constraints hinder these processes, and
hence the rate of scientific progress. This paper presents an active perception
approach that aims to reduce robots' reliance on human supervision and improve
science productivity by encoding scientists' domain knowledge and decision
making process on-board. We use Bayesian networks to compactly model critical
aspects of scientific knowledge while remaining robust to observation and
modeling uncertainty. We then formulate path planning and sensor scheduling as
an information gain maximization problem, and propose a sampling-based solution
based on Monte Carlo tree search to plan informative sensing actions which
exploit the knowledge encoded in the network. The computational complexity of
our framework does not grow with the number of observations taken and allows
long horizon planning in an anytime manner, making it highly applicable to
field robotics. Simulation results show statistically significant performance
improvements over baseline methods, and we validate the practicality of our
approach through both hardware experiments and simulated experiments with field
data gathered during the NASA Mojave Volatiles Prospector science expedition.
