Browsing by Author "Rademeyer, Jaak"
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- ItemVision-based flight control for a quadrotor UAV(Stellenbosch : Stellenbosch University, 2020-03) Rademeyer, Jaak; Engelbrecht, J. A. A.; Engelbrecht, H. A.; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.ENGLISH ABSTRACT: This thesis presents the development, implementation, and practical verification of a vision-based flight control and waypoint navigation system for a quadrotor unmanned aerial vehicle (UAV). The vision-based flight control system was developed to serve as a building block in a larger project to autonomously navigate an inspection drone relative to an inspection target in an indoor or GPS-denied environment. The intended application of this technology is to use autonomous drones to inspect large commercial airliners for external damage while the aircraft is parked in a maintenance hangar. For the project, a vision-based UAV research platform was created using commercial off-the-shelf UAV hardware and open-source software. The Intel Aero RTF Drone was used as the research vehicle, the PX4 open-source software was used for flight control and state estimation, the Robotics Operating System (ROS) and the ArUco library was used for vision-based position and attitude determination, QGroundControl software was used for the ground control station, and the Gazebo software was used to create a simulation environment that supports both software-in-the-loop and hardware-in-theloop simulations. The vision-based flight control system was developed by modifying the PX4 flight control software to replace the existing GPS-based state estimator with our own visionbased state estimator, and adding vision-based pose estimation software that executes on a companion computer and determines the quadrotor position and attitude using external ArUco markers. The PX4 flight control architecture was also reverse-engineered and the controller gains were re-designed for the Intel Aero RTF flight dynamics. Finally, a waypoint scheduler was implemented to enable the quadrotor UAV to autonomously navigate a pre-determined set of position waypoints around an inspection target. The vision-based flight control system was verified with laboratory experiments, simulations, and practical flight tests. The practical flight tests showed that the vision-based pose estimation reliably detects the ArUco markers and provides position and attitude measurements even during aggressive position and yaw angle steps. The vision-based state estimator successfully estimates the position, velocity, and attitude of the quadrotor UAV, and propagates the state when markers are temporarily lost from the camera’s field of view. The flight tests also demonstrated that the vision-based flight control and waypoint navigation system provides stable and accurate position control for the quadrotor UAV and successfully navigates the vehicle to follow a given sequence of position.