  We present a method for solving the coverage problem with the objective of
autonomously exploring an unknown environment under mission time constraints.
Here, the robot is tasked with planning a path over a horizon such that the
accumulated area swept out by its sensor footprint is maximized. Because this
problem exhibits a diminishing returns property known as submodularity, we
choose to formulate it as a tree-based sequential decision making process. This
formulation allows us to evaluate the effects of the robot's actions on future
world coverage states, while simultaneously accounting for traversability risk
and the dynamic constraints of the robot. To quickly find near-optimal
solutions, we propose an effective approximation to the coverage sensor model
which adapts to the local environment. Our method was extensively tested across
various complex environments and served as the local exploration algorithm for
a competing entry in the DARPA Subterranean Challenge.
