  In this paper, we address the problem of stochastic motion planning under
partial observability, more specifically, how to navigate a mobile robot
equipped with continuous range sensors such as LIDAR. In contrast to many
existing robotic motion planning methods, we explicitly consider the
uncertainty of the robot state by modeling the system as a POMDP. Recent work
on general purpose POMDP solvers is typically limited to discrete observation
spaces, and does not readily apply to the proposed problem due to the
continuous measurements from LIDAR. In this work, we build upon an existing
Monte Carlo Tree Search method, POMCP, and propose a new algorithm POMCP++. Our
algorithm can handle continuous observation spaces with a novel measurement
selection strategy. The POMCP++ algorithm overcomes over-optimism in the value
estimation of a rollout policy by removing the implicit perfect state
assumption at the rollout phase. We validate POMCP++ in theory by proving it is
a Monte Carlo Tree Search algorithm. Through comparisons with other methods
that can also be applied to the proposed problem, we show that POMCP++ yields
significantly higher success rate and total reward.
