  Informative path planning is an important and challenging problem in robotics
that remains to be solved in a manner that allows for wide-spread
implementation and real-world practical adoption. Among various reasons for
this, one is the lack of approaches that allow for informative path planning in
high-dimensional spaces and non-trivial sensor constraints. In this work we
present a sampling-based approach that allows us to tackle the challenges of
large and high-dimensional search spaces. This is done by performing informed
sampling in the high-dimensional continuous space and incorporating potential
information gain along edges in the reward estimation. This method rapidly
generates a global path that maximizes information gain for the given path
budget constraints. We discuss the details of our implementation for an example
use case of searching for multiple objects of interest in a large search space
using a fixed-wing UAV with a forward-facing camera. We compare our approach to
a sampling-based planner baseline and demonstrate how our contributions allow
our approach to consistently out-perform the baseline by 18.0%. With this we
thus present a practical and generalizable informative path planning framework
that can be used for very large environments, limited budgets, and high
dimensional search spaces, such as robots with motion constraints or
high-dimensional configuration spaces.
