The purpose of this book is to give necessary and sufficient theoretical basis for those interested in working with Unmanned Aerial Vehicles (UAVs). Likewise, it provides, for those working in this area, a substantial theoretical and practical complement to their work.

When we work in autonomous navigation for aerial vehicles, it is common to consider ideal cases, i.e., full knowledge of the states, ideal measurements of the sensors, and non-external (or known) perturbations. Nevertheless, in flight tests this is not the case. The book's benefits to the audience are several: first of all, we propose three different approaches to mathematically represent the dynamics of an aerial vehicle. In one of these methodologies the quaternion technique is used to solve the singularity problem in UAVs. Secondly, detailed information is provided about how to fuse inertial data for attitude estimation with the results comparable to those of an expensive and commercial IMU (Inertial Measurement Unit). In addition, the book proposes substantial theoretical and practical validation to improve dropped or noisy signals. This part is crucial when using commercial sensors in handmade aerial prototypes. The UAV localization problem in this book is tackled by proposing an observer-control scheme using only the basic sensors in a drone.

For those dedicated to control systems, different control strategies, from classical to modern algorithms, haven been proposed using various approaches. One of the goals settled while writing this book was to give to the reader a wide spectrum of techniques so he/she could choose the most appropriate for his/her needs. Algorithm design considers a possible outdoor application, i.e., robustness with respect to unknown perturbations such as wind. Last but not least, three tools are given to improve autonomous navigation or to assist the manual pilot. The first one considers specific tasks in defined conditions (time, velocity, etc.) to generate a trajectory and follows it using an UAV. The second tool is used to avoid crashes when obstacles are present along the trajectory. The last approach considers a situation when the UAV is performing a semi-autonomous mission and its pilot suddenly loses sight of the vehicle. Here the vehicle states are sent to the pilot to provide him/her with more information about the vehicle's flight conditions, by means of a haptic joystick, and to improve its performance (teleoperation mode).

A quadrotor vehicle is chosen as an aerial configuration in this book due to its popularity among researchers. However, all the algorithms proposed may be adapted to work with different aerial configurations. Our results are validated in simulations, real time or flight tests on different platforms (commercial or handmade aerial prototypes) and with different sensors, and this makes the book valuable.