  We present an integrated Task-Motion Planning (TMP) framework for navigation
in large-scale environments. Of late, TMP for manipulation has attracted
significant interest resulting in a proliferation of different approaches. In
contrast, TMP for navigation has received considerably less attention.
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 objects to procure, the regions to navigate to
in order to acquire them; 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. In this paper, we discuss a
probabilistically complete approach that leverages this task-motion interaction
for navigating in large knowledge-intensive domains, returning a plan that is
optimal at the task-level. The framework is intended for motion planning under
motion and sensing uncertainty, which is formally known as belief space
planning. The underlying methodology is validated in simulation, in an office
environment and its scalability is tested in the larger Willow Garage world. A
reasonable comparison with a work that is closest to our approach is also
provided. We also demonstrate the adaptability of our approach by considering a
building floor navigation domain. Finally, we also discuss the limitations of
our approach and put forward suggestions for improvements and future work.
