Drone performance modelling for U-space services
Tutor/a - Director/a
Estudiant
Peña Chavez, Wilfredo Javier
Tipus de document
Projecte Final de Màster Oficial
Data
2024
rights
Accés obert
Editorial
Universitat Politècnica de Catalunya
UPCommons
Resum
Thanks to the wide variety of applications offered by drones, their density has been increasing. This growth in their number could cause consequences, such as possible collisions between these devices. The way to avoid collisions in a densely populated environment is to know the trajectory of moving objects. Although it is true that there are already models that can predict the trajectory of a drone, these represent a very large computational cost, which is why this document has as its main objective to predict the trajectory of drones in a U-space environment in a simple way. The document presents a study of the advances in the field of trajectory prediction (both for airplanes and drones), the concept of U-space, the kinematics of objects and the mathematical modeling of the object in question. The literature review raises the possible tools used to know the trajectories such as state estimation, kinematic or machine learning models. It is also necessary to know the concept of the U-space environment, where the definition of the concept, regulations within the environment and the flight phases that a drone must perform (pre-flight, in-flight and post-flight) are shown. Because the model is composed of particle kinematics equations, a chapter was added where the concepts of each of the equations are explained in both accelerated and non-accelerated models. The document also contains the main idea, which is the mathematical model. This mathematical model is developed in several sections of the path. The length of the path will depend on the characteristics of altitudes, lengths, inclinations and turns. In these small paths, the mixture of kinematics concepts is used together with the graphical behavior of turns and inclinations. To develop this model, a mixed sampling approach was used, where the behavior was numerically analyzed to ensure compatibility with the kinematics and the numerical history of the simulation in Mission Planner.Finally, each of the small trajectories is tested, which yields relative errors of less than five percentage points.
