  The ability to traverse an unknown environment is crucial for autonomous
robot operations. However, due to the limited sensing capabilities and system
constraints, approaching this problem with a single robot agent can be slow,
costly, and unsafe. For example, in planetary exploration missions, the wear on
the wheels of a rover from abrasive terrain should be minimized at all costs as
reparations are infeasible. On the other hand, utilizing a scouting robot such
as a micro aerial vehicle (MAV) has the potential to reduce wear and time costs
and increasing safety of a follower robot. This work proposes a novel
cooperative IPP framework that allows a scout (e.g., an MAV) to efficiently
explore the minimum-cost-path for a follower (e.g., a rover) to reach the goal.
We derive theoretic guarantees for our algorithm, and prove that the algorithm
always terminates, always finds the optimal path if it exists, and terminates
early when the found path is shown to be optimal or infeasible. We show in
thorough experimental evaluation that the guarantees hold in practice, and that
our algorithm is 22.5% quicker to find the optimal path and 15% quicker to
terminate compared to existing methods.
