  We present an integrated Task-Motion Planning (TMP) framework for navigation
in large-scale environment. Autonomous robots operating in real world complex
scenarios require planning in the discrete (task) space and the continuous
(motion) space. In knowledge intensive domains, on the one hand, a robot has to
reason at the highest-level, for example the regions to navigate to; on the
other hand, the feasibility of the respective navigation tasks have to be
checked at the execution level. This presents a need for motion-planning-aware
task planners. We discuss a probabilistically complete approach that leverages
this task-motion interaction for navigating in indoor domains, returning a plan
that is optimal at the task-level. Furthermore, our framework is intended for
motion planning under motion and sensing uncertainty, which is formally known
as belief space planning. The underlying methodology is validated with a
simulated office environment in Gazebo. In addition, we discuss the limitations
and provide suggestions for improvements and future work.
