  This paper investigates the usefulness of reasoning about the uncertain
presence of obstacles during path planning, which typically stems from the
usage of probabilistic occupancy grid maps for representing the environment
when mapping via a noisy sensor like a stereo camera. The traditional planning
paradigm prescribes using a hard threshold on the occupancy probability to
declare that a cell is an obstacle, and to plan a single path accordingly while
treating unknown space as free. We compare this approach against a new
uncertainty-aware planner, which plans two different path hypotheses and then
merges their initial trajectory segments into a single one ending in a
"next-best view" pose. After this informative view is taken, the planner
commits to one of the hypotheses, or to a completely new one if a collision is
imminent. Simulations were conducted comparing the proposed and traditional
planner. Results show the existence of planning scenarios -- like when the
environment contains a dead-end, or when the goal is placed close to an
obstacle -- in which reasoning about uncertainty can significantly decrease the
robot's traveled distance and increase the chances of reaching the goal. The
new planner was also validated on a real Clearpath Jackal robot equipped with a
ZED 2 stereo camera.
