Model Predictive Path Integral Control Framework for Partially Observable Navigation: A Quadrotor Case Study
This addresses navigation challenges for robotics applications where environments are often partially observable, though it appears incremental as it extends an existing method to more scenarios.
The paper tackles autonomous navigation in partially observable environments by proposing a generic Model Predictive Path Integral control framework that works for 2D or 3D tasks, and it demonstrates perfect performance in cluttered environments using quadrotor simulations.
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.