Path Planning for a UAV by Considering Motion Model Uncertainty

The primary purpose of path planning for unmanned aerial vehicles (UAVs), which is a necessary prerequisite toward an autonomous UAV, is to guide the robot to the predefined target while the chosen path is optimized. This paper addresses the problem of path planning for an unmanned aerial vehicle in a 2D indoor environment, considering motion uncertainty. To cope with this challenge, the problem of motion planning is formulated in three parts. A vision-based extended Kalman Filter (EKF) is used to localize the UAV in the unstructured environment. To overcome motion uncertainty, the problem is modeled as a Markov decision problem (MDP). Finally, a novel dynamic feedback linearization based switching controller is proposed for point-to-point motion. Simulation and experimental results are given to show the effectiveness of the proposed path planning method in practice.