  Recently, Model Predictive Path Integral (MPPI) control algorithm has been
extensively applied to autonomous navigation tasks, where the cost map is
mostly assumed to be known and the 2D navigation tasks are only performed. In
this paper, we propose a generic MPPI control framework that can be used for 2D
or 3D autonomous navigation tasks in either fully or partially observable
environments, which are the most prevalent in robotics applications. This
framework exploits directly the 3D-voxel grid acquired from an on-board sensing
system for performing collision-free navigation. We test the framework, in
realistic RotorS-based simulation, on goal-oriented quadrotor navigation tasks
in a cluttered environment, for both fully and partially observable scenarios.
Preliminary results demonstrate that the proposed framework works perfectly,
under partial observability, in 2D and 3D cluttered environments.
