How hard is it to cross the room? -- Training (Recurrent) Neural Networks to steer a UAV

This work explores the feasibility of steering a drone with a (recurrent) neural network, based on input from a forward looking camera, in the context of a high-level navigation task. We set up a generic framework for training a network to perform navigation tasks based on imitation learning. It can be applied to both aerial and land vehicles. As a proof of concept we apply it to a UAV (Unmanned Aerial Vehicle) in a simulated environment, learning to cross a room containing a number of obstacles. So far only feedforward neural networks (FNNs) have been used to train UAV control. To cope with more complex tasks, we propose the use of recurrent neural networks (RNN) instead and successfully train an LSTM (Long-Short Term Memory) network for controlling UAVs. Vision based control is a sequential prediction problem, known for its highly correlated input data. The correlation makes training a network hard, especially an RNN. To overcome this issue, we investigate an alternative sampling method during training, namely window-wise truncated backpropagation through time (WW-TBPTT). Further, end-to-end training requires a lot of data which often is not available. Therefore, we compare the performance of retraining only the Fully Connected (FC) and LSTM control layers with networks which are trained end-to-end. Performing the relatively simple task of crossing a room already reveals important guidelines and good practices for training neural control networks. Different visualizations help to explain the behavior learned.Comment: 12 pages, 30 figure

A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

Navigation, localization and stabilization of formations of unmanned aerial and ground vehicles

A leader-follower formation driving algorithm developed for control of heterogeneous groups of unmanned micro aerial and ground vehicles stabilized under a top-view relative localization is presented in this paper. The core of the proposed method lies in a novel avoidance function, in which the entire 3D formation is represented by a convex hull projected along a desired path to be followed by the group. Such a representation of the formation provides non-collision trajectories of the robots and respects requirements of the direct visibility between the team members in environment with static as well as dynamic obstacles, which is crucial for the top-view localization. The algorithm is suited for utilization of a simple yet stable visual based navigation of the group (referred to as GeNav), which together with the on-board relative localization enables deployment of large teams of micro-scale robots in environments without any available global localization system. We formulate a novel Model Predictive Control (MPC) based concept that enables to respond to the changing environment and that provides a robust solution with team members' failure tolerance included. The performance of the proposed method is verified by numerical and hardware experiments inspired by reconnaissance and surveillance missions

Unmanned Aerial Vehicle (UAV) mission planning based on Fast Marching Square (FM²) planner and Differential Evolution (DE)

Nowadays, mission planning for Unmanned Aerial Vehicles (UAVs) is a very attractive research field. UAVs have been a research focus for many purposes. In military and civil fields, the UAVs are very used for different missions. Many of these studies require a path planning to perform autonomous flights. Several problems related to the physical limitations of the UAV arise when the planning is carried out, as well as the maintenance of a fixed flight level with respect to the ground to capture videos or overlying images. This work presents an approach to plan missions for UAVs keeping a fixed flight level constraint. An approach is proposed to solve these problems and to generate effective paths in terms of smoothness and safety distance in two different types of environments: 1) 3D urban environments and 2) open field with non-uniform terrain environments. Many proposed activities to be carried out by UAVs in whatever the environment require a control over the altitude for different purposes: energy saving and minimization of costs are some of these objectives. In general terms, the planning is required to avoid all obstacles encountered in the environment and to maintain a fixed flight level during the path execution. For this reason, a mission planning requires robust planning methods. The method used in this work as planner is the Fast Marching Square (FM2) method, which generates a path free of obstacles. As a novelty, the method proposed includes two adjustment parameters. Depending on the values of these parameters, the restriction of flight level can be modified, as well as the smoothness and safety margins from the obstacles of the generated paths. The Dubins airplane model is used to check if the path resulting from the FM2 is feasible according to the constraints of the UAV: its turning rate, climb rate and cruise speed. Besides, this research also presents a novel approach for missions of Coverage Path Planning (CPP) carried out by UAVs in 3D environments. These missions are focused on path planning to cover a certain area in an environment in order to carry out tracking, search or rescue tasks. The methodology followed uses an optimization process based on the Differential Evolution (DE) algorithm in combination with the FM2 planner. Finally, the UAVs formation problem is introduced and addressed in a first stage using the planner proposed in this thesis. A wide variety of simulated experiments have been carried out to illustrate the efficiency and robustness of the approaches presented, obtaining successful results in different urban and open field 3D environments.Hoy en día la planificación de misiones para vehículos aéreos no tripulados (UAV) es un campo de investigación muy atractivo. Los UAV son foco de investigación en numerosas aplicaciones, tanto en el campo civil como militar. Muchas de estas aplicaciones requieren de un sistema de planificación de ruta que permita realizar vuelos autónomos y afrontar problemas relacionados con las limitaciones físicas del UAV y con requerimientos como el nivel de vuelo sobre el suelo para, entre otras funciones, poder capturar videos o imágenes. Este trabajo presenta una propuesta de planificador para vehículos aéreos no tripulados que permite resolver los problemas citados previamente, incluyendo en la planificación las consideraciones cinemáticas del UAV y las restricciones de nivel de vuelo, generando rutas suaves, realizables y suficientemente seguras para dos tipos diferentes de entornos 3D: 1) entornos urbanos y 2) campos abiertos con terrenos no uniformes. El método utilizado en esta tesis como base para la planificación es el método Fast Marching Square (FM2), que genera un camino libre de obstáculos. Como novedad, el método propuesto incluye dos parámetros de ajuste. Dependiendo de los valores de estos parámetros, se puede modificar la restricción de nivel de vuelo, así como la suavidad y los márgenes de seguridad respecto a los obstáculos de las rutas generadas. El modelo cinemático de Dubins se utiliza para verificar si la ruta resultante de nuestro planificador es realizable de acuerdo con las restricciones del UAV: su velocidad de giro, velocidad de ascenso y velocidad de crucero. Además, esta tesis también presenta una propuesta novedosa para la planificación de misiones de Coverage Path Planning (CPP) en entornos 3D. Estas misiones se centran en la planificación de rutas para cubrir un área determinada de un entorno con el fin de llevar a cabo tareas de rastreo, búsqueda o rescate. La metodología seguida utiliza un proceso de optimización basado en el algoritmo Differential Evolution (DE) en combinación con nuestro planificador FM2. Como parte final de la tesis, el problema de formación de UAVs se introduce y aborda en una primera etapa utilizando el planificador FM2 propuesto.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Antonio Giménez Fernández.- Secretario: Luis Santiago Garrido Bullón.- Vocal: Raúl Suárez Feijó