Modeling, identification and navigation of autonomous air vehicles

Abstract: During the last few years Unmanned Air Vehicles have seen a widespread utilization, both in civilian and military scenarios, because of the benefits of replacing the human presence in unsuitable or hostile environments and dangerous or dull tasks. Examples of their use are surveillance, firefighting, rescuing, extreme photography and environmental monitoring. The main interest of this work is autonomous navigation of such air vehicles, specifically quadrotor helicopters (quadrocopters), and the focus is on convergence to a target destination with collision avoidance. In this work, a general model for a quadrocopter UAV is obtained, making use of a first principles modeling approach, and system identification is exploited in order to relate in a suitable manner the control signals to the effective behavior of the vehicle. The main contribution is the design of a controller for convergence and avoidance of static obstacles, based both on considerations on the dynamics of the agent and knowledge of the testbed for the experiments. The controller is composed of a layered structure. The external layer consists in the computation of a collision-free path leading to the target position and is based on a navigation function approach. The inner layer is meant to make the vehicle follow the waypoints imposed by the outer layer and thus consists in a position controller. Experiments have been conducted in different scenarios in order to analyze the behavior of the controlled system. The final part of the work regards the design of a controller for 3D navigation and collision avoidance for an air vehicle with more constrained dynamics in respect to the quadrocopter. This controller exploits both dipolar navigation functions and model predictive control in order to obtain the control inputs that safely lead the vehicle to its destination with the desired orientation.