Control Design and Visual Autonomous Navigation of Quadrotor

Abstract

In this paper, an autonomous navigation and obstacle avoidance system based on monocular camera has been designed and implemented which enables the quadrotor to navigate in a previously unknown GPS-denied environment. Moreover, four controllers have been designed, simulated and their performance were compared. The various control strategies are LQR, PID, Feedback Linearization with pole placement and Sliding Mode control.Sensor data and the camera video stream have been used by a Key-frame visual simultaneous localization and mapping (SLAM) system to compute the location of the drone and generate the 3D map of the environment in the form of point cloud. This point cloud data is clustered and used for obstacle detection. Moreover a probabilistic roadmap (PRM) algorithm has been used to generate a collision-free path that will be followed by the drone based on the chosen controller.We implemented our approach on a Parrot ARDrone2.0, and the theoretical results have been validated with experiments. All computations are performed on a ground station, which is connected to the drone via wireless LAN