  Motion planning is challenging when it comes to the case of imperfect state
information. Decision should be made based on belief state which evolves
according to the noise from the system dynamics and sensor measurement. In this
paper, we propose the QV-Tree Search algorithm which combines the state-of-art
offline and online approximation methods for POMDP. Instead of full node
expansions in the tree search, only probable future observations are considered
through forward sampling. This modification helps reduce online computation
time and allows for GPU acceleration. We show using repre- sentative examples
that the proposed QV-Tree Search is able to actively localize the robot in
order to reach the goal location with high probability. The results of the
proposed method is also compared with the A* and MDP algorithms, neither of
which handles state uncertainty directly. The comparison shows that QV-Tree
Search is able to drive the robot to the goal with higher success rate and
fewer steps.
