Autonomous Landing of an Unmanned Aerial Vehicle using Image-Based Fuzzy Control

This paper presents a vision based autonomous landing control approach for unmanned aerial vehicles (UAV). The 3D position of an unmanned helicopter is estimated based on the homographies estimated of a known landmark. The translation and altitude estimation of the helicopter against the helipad position are the only information that is used to control the longitudinal, lateral and descend speeds of the vehicle. The control system approach consists in three Fuzzy controllers to manage the speeds of each 3D axis of the aircraft s coordinate system. The 3D position estimation was proven rst, comparing it with the GPS + IMU data with very good results. The robust of the vision algorithm against occlusions was also tested. The excellent behavior of the Fuzzy control approach using the 3D position estimation based in homographies was proved in an outdoors test using a real unmanned helicopter.

Evaluation of mission planning strategies of a robotic aerial vehicle for the AIRC international competition

The International Aerial Robotics Competition (IARC) is an important event where teams from universities design flying autonomous vehicles to overcome the last challenges in the field. The goal of the Seventh Mission proposed by the IARC is to guide several mobile ground robots to a target area. The scenario is complex and not determinist due to the random behavior of the ground robots movement. The UAV must select efficient strategies to complete the mission. The goal of this work has been evaluating different alternative mission planning strategies of a UAV for this competition. The Mission Planner component is in charge of taking the UAV decisions. Different strategies have been developed and evaluated for the component, achieving a better performance Mission Planner and valuable knowledge about the mission. For this purpose, it was necessary to develop a simulator to evaluate the different strategies. The simulator was built as an improvement of an existing previous version.

Analysis of air flow past and through the 2415-3S airfoil for an unmanned aerial vehicle with internal propulsion system

This paper deals with the prediction of velocity fields on the 2415-3S airfoil which will be used for an unmanned aerial vehicle with internal propulsion system and in this way analyze the air flow through an internal duct of the airfoil using computational fluid dynamics. The main objective is to evaluate the effect of the internal air flow past the airfoil and how this affects the aerodynamic performance by means of lift and drag forces. For this purpose, three different designs of the internal duct were studied; starting from the base 2415-3S airfoil developed in previous investigation, basing on the hypothesis of decreasing the flow separation produced when the propulsive airflow merges the external flow, and in this way obtaining the best configuration. For that purpose, an exhaustive study of the mesh sensitivity was performed. It was used a non-structured mesh since the computational domain is three-dimensional and complex. The selected mesh contains approximately 12.5 million elements. Both the computational domain and the numerical solution were made with commercial CAD and CFD software, respectively. Air, incompressible and steady was analyzed. The boundary conditions are in concordance with experimental setup in the AF 6109 wind tunnel. The k-e model is utilized to describe the turbulent flow process as followed in references. Results allowed obtaining velocity contours as well as lift and drag coefficients and also the location of separation and reattachment regions in some cases for zero degrees of angle of attack on the internal and external surfaces of the airfoil. Finally, the selection of the configuration with the best aerodynamic performance was made, selecting the option without curved baffles.

Numerical analysis of air flow past the 2415-3S airfoil for an unmanned aerial vehicle with internal propulsion system

This paper deals with the prediction of pressure and velocity fields on the 2415-3S airfoil which will be used for and unmanned aerial vehicle with internal propulsion system and in this way analyze the air flow through an internal duct of the airfoil using computational fluid dynamics. The main objective is to evaluate the effect of the internal air flow past the airfoil and how this affects the aerodynamic performance by means of lift and drag forces. For this purpose, three different designs of the internal duct were studied; starting from the base 2415-3S airfoil developed in previous investigation, basing on the hypothesis of decreasing the flow separation produced when the propulsive airflow merges the external flow, and in this way obtaining the best configuration. For that purpose, an exhaustive study of the mesh sensitivity was performed. It was used a non-structured mesh since the computational domain is tridimensional and complex. The selected mesh contains approximately 12.5 million elements. Both the computational domain and the numerical solution were made with commercial CAD and CFD software respectively. Air, incompressible and steady was analyzed. The boundary conditions are in concordance with experimental setup in the AF 6109 wind tunnel. The k-ε model is utilized to describe the turbulent flow process as followed in references. Results allowed obtaining pressure and velocity contours as well as lift and drag coefficients and also the location of separation and reattachment regions in some cases for zero degrees of angle of attack on the internal and external surfaces of the airfoil. Finally, the selection of the configuration with the best aerodynamic performance was made, selecting the option without curved baffles.

3D Object following based on visual information for Unmanned Aerial Vehicles

This article presents a novel system and a control strategy for visual following of a 3D moving object by an Unmanned Aerial Vehicle UAV. The presented strategy is based only on the visual information given by an adaptive tracking method based on the color information, which jointly with the dynamics of a camera fixed to a rotary wind UAV are used to develop an Image-based visual servoing IBVS system. This system is focused on continuously following a 3D moving target object, maintaining it with a fixed distance and centered on the image plane. The algorithm is validated on real flights on outdoors scenarios, showing the robustness of the proposed systems against winds perturbations, illumination and weather changes among others. The obtained results indicate that the proposed algorithms is suitable for complex controls task, such object following and pursuit, flying in formation, as well as their use for indoor navigation

Aerial Object Following Using Visual Fuzzy Servoing

This article presents a visual servoing system to follow a 3D moving object by a Micro Unmanned Aerial Vehicle (MUAV). The presented control strategy is based only on the visual information given by an adaptive tracking method based on the colour information. A visual fuzzy system has been developed for servoing the camera situated on a rotary wing MAUV, that also considers its own dynamics. This system is focused on continuously following of an aerial moving target object, maintaining it with a fixed safe distance and centred on the image plane. The algorithm is validated on real flights on outdoors scenarios, showing the robustness of the proposed systems against winds perturbations, illumination and weather changes among others. The obtained results indicate that the proposed algorithms is suitable for complex controls task, such object following and pursuit, flying in formation, as well as their use for indoor navigation

Fuel Cells: A Real Option for Unmanned Aerial Vehicles Propulsion

The possibility of implementing fuel cell technology in Unmanned Aerial Vehicle (UAV) propulsion systems is considered. Potential advantages of the Proton Exchange Membrane or Polymer Electrolyte Membrane (PEMFC) and Direct Methanol Fuel Cells (DMFC), their fuels (hydrogen and methanol), and their storage systems are revised from technical and environmental standpoints. Some operating commercial applications are described. Main constraints for these kinds of fuel cells are analyzed in order to elucidate the viability of future developments. Since the low power density is the main problem of fuel cells, hybridization with electric batteries, necessary in most cases, is also explored.

Using the Cross-Entropy Method for Control Optimization: A Case Study of See-and-Avoid on Unmanned Aerial Vehicles

This paper presents an adaptation of the Cross-Entropy (CE) method to optimize fuzzy logic controllers. The CE is a recently developed optimization method based on a general Monte-Carlo approach to combinatorial and continuous multi-extremal optimization and importance sampling. This work shows the application of this optimization method to optimize the inputs gains, the location and size of the different membership functions' sets of each variable, as well as the weight of each rule from the rule's base of a fuzzy logic controller (FLC). The control system approach presented in this work was designed to command the orientation of an unmanned aerial vehicle (UAV) to modify its trajectory for avoiding collisions. An onboard looking forward camera was used to sense the environment of the UAV. The information extracted by the image processing algorithm is the only input of the fuzzy control approach to avoid the collision with a predefined object. Real tests with a quadrotor have been done to corroborate the improved behavior of the optimized controllers at different stages of the optimization process.

Aerodynamic study of airfoils geometric imperfections at low Reynolds number

The interest of this study is based on the observation that some manufacturing processes of various vehicles wings, such as unmanned aerial vehicle (UAV), or blades, such as wind turbine blades, or other devices that use aerodynamic profiles, produce imperfections in the leading edge or open trailing edge with bigger thickness than original airfoil, because, for example, they are manufactured in two parts, top surface and bottom surface and subsequently joined. In this last step might appear a sliding between the top surface and the bottom surface having a small step on the leading edge or a small thickness gain can occur on the trailing edge. Normally these imperfections are corrected through a refill and/or sanding processes using many hours of manual labor. Therefore the initial objective of this research is to determine the level of influence in the aerodynamic characteristics at low Reynolds numbers (Lissaman, 1981, Carmichael, 1981, Nagamatsu and Cuche, 1981, Schmitz, 1957, Cebeci, 1989, Mueller and Batill, 1982) of these imperfections in the manufacture, and determine whether there may be a value for which it would not be necessary to correct them

Biomechanics of smart wings in a bat robot: morphing wings using SMA actuators

This paper presents the design of a bat-like micro aerial vehicle with actuated morphing wings. NiTi shape memory alloys (SMAs) acting as artificial biceps and triceps muscles are used for mimicking the morphing wing mechanism of the bat flight apparatus. Our objective is twofold. Firstly, we have implemented a control architecture that allows an accurate and fast SMA actuation. This control makes use of the electrical resistance measurements of SMAs to adjust morphing wing motions. Secondly, the feasibility of using SMA actuation technology is evaluated for the application at hand. To this purpose, experiments are conducted to analyze the control performance in terms of nominal and overloaded operation modes of the SMAs. This analysis includes: (i) inertial forces regarding the stretchable wing membrane and aerodynamic loads, and (ii) uncertainties due to impact of airflow conditions over the resistance–motion relationship of SMAs. With the proposed control, morphing actuation speed can be increased up to 2.5 Hz, being sufficient to generate lift forces at a cruising speed of 5ms−1.

Towards a ground navigation system based in visual feedback provided by a mini UAV

This paper addresses initial efforts to develop a navigation system for ground vehicles supported by visual feedback from a mini aerial vehicle. A visual-based algorithm computes the ground vehicle pose in the world frame, as well as possible obstacles within the ground vehicle pathway. Relying on that information, a navigation and obstacle avoidance system is used to re-plan the ground vehicle trajectory, ensuring an optimal detour. Finally, some experiments are presented employing a unmanned ground vehicle (UGV) and a low cost mini unmanned aerial vehicle (UAV).

See-and-avoid quadcopter using fuzzy control optimized by cross-entropy

In this work we present an optimized fuzzy visual servoing system for obstacle avoidance using an unmanned aerial vehicle. The cross-entropy theory is used to optimise the gains of our controllers. The optimization process was made using the ROS-Gazebo 3D simulation with purposeful extensions developed for our experiments. Visual servoing is achieved through an image processing front-end that uses the Camshift algorithm to detect and track objects in the scene. Experimental flight trials using a small quadrotor were performed to validate the parameters estimated from simulation. The integration of crossentropy methods is a straightforward way to estimate optimal gains achieving excellent results when tested in real flights.

Fusion of probabilistic knowledge-based classification rules and learning automata for automatic recognition of digital images

In this paper, the fusion of probabilistic knowledge-based classification rules and learning automata theory is proposed and as a result we present a set of probabilistic classification rules with self-learning capability. The probabilities of the classification rules change dynamically guided by a supervised reinforcement process aimed at obtaining an optimum classification accuracy. This novel classifier is applied to the automatic recognition of digital images corresponding to visual landmarks for the autonomous navigation of an unmanned aerial vehicle (UAV) developed by the authors. The classification accuracy of the proposed classifier and its comparison with well-established pattern recognition methods is finally reported.

Arquitectura cognitiva híbrida para la navegación autónoma de UAVs mediante mapas topológicos visuales

El objetivo fundamental de la presente tesis doctoral es el diseño de una arquitectura cognitiva, que pueda ser empleada para la navegación autónoma de vehículos aéreos no tripulados conocidos como UAV (Unmanned Aerial Vehicle). Dicha arquitectura cognitiva se apoya en la definición de una librería de comportamientos, que aportarán la inteligencia necesaria al UAV para alcanzar los objetivos establecidos, en base a la información sensorial recopilada del entorno de operación. La navegación autónoma del UAV se apoyará en la utilización de un mapa topológico visual, consistente en la definición de un grafo que engloba mediante nodos los diferentes landmarks ubicados en el entorno, y que le servirán al UAV de guía para alcanzar su objetivo. Los arcos establecidos entre los nodos del mapa topológico, le proporcionarán de la información necesaria para establecer el rumbo más adecuado para alcanzar el siguiente landmark a visitar, siguiendo siempre una secuencia lógica de navegación, basada en la distancia entre un determinado landmark con respecto al objetivo final ó landmark destino. La arquitectura define un mecanismo híbrido de control, el cual puede conmutar entre dos diferentes modos de navegación. El primero es el denominado como Search Mode, el cual se activará cuando el UAV se encuentre en un estado desconocido dentro del entorno, para lo cual hará uso de cálculos basado en la entropía para la búsqueda de posibles landmarks. Se empleará como estrategia novedosa la idea de que la entropía de una imagen tiene una correlación directa con respecto a la probabilidad de que dicha imagen contenga uno ó varios landmarks. De esta forma, la estrategia para la búsqueda de nuevos landmarks en el entorno, se basará en un proceso continuo de maximización de la entropía. Si por el contrario el UAV identifica la existencia de un posible landmark entre los definidos en su mapa topológico, se considerará que está sobre un estado conocido, por lo que se conmutará al segundo modo de navegación denominado como Homing Mode, el cual se encargará de calcular señales de control para la aproximación del UAV al landmark localizado. Éste último modo implementa un control dual basado en dos tipos de controladores (FeedForward/FeedBack) que mediante su combinación, aportarán al UAV señales de control cada vez más óptimas, además de llevar a cabo un entrenamiento continuo y en tiempo real. Para cumplir con los requisitos de ejecución y aprendizaje en tiempo real de la arquitectura, se han tomado como principales referencias dos paradigmas empleados en diferentes estudios dentro del área de la robótica, como son el paradigma de robots de desarrollo (developmental robots) basado en un aprendizaje del robot en tiempo real y de forma adaptativa con su entorno, así como del paradigma de modelos internos (internal models) basado en los resultados obtenidos a partir de estudios neurocientíficos del cerebelo humano; dicho modelo interno sirve de base para la construcción del control dual de la arquitectura. Se presentarán los detalles de diseño e implementación de los diferentes módulos que componen la arquitectura cognitiva híbrida, y posteriormente, los diferentes resultados obtenidos a partir de las pruebas experimentales ejecutadas, empleando como UAV la plataforma robótica aérea de AR.Drone. Como resultado final se ha obtenido una validación completa de la arquitectura cognitiva híbrida objetivo de la tesis, cumplimento con la totalidad de requisitos especificados y garantizando su viabilidad como aplicación operativa en el mundo real. Finalmente, se muestran las distintas conclusiones a las cuales se ha llegado a partir de los resultados experimentales, y se presentan las diferentes líneas de investigación futuras que podrán ser ejecutadas.

New generation of human machine interfaces for controlling UAV through depth based gesture recognition

New forms of natural interactions between human operators and UAVs (Unmanned Aerial Vehicle) are demanded by the military industry to achieve a better balance of the UAV control and the burden of the human operator. In this work, a human machine interface (HMI) based on a novel gesture recognition system using depth imagery is proposed for the control of UAVs. Hand gesture recognition based on depth imagery is a promising approach for HMIs because it is more intuitive, natural, and non-intrusive than other alternatives using complex controllers. The proposed system is based on a Support Vector Machine (SVM) classifier that uses spatio-temporal depth descriptors as input features. The designed descriptor is based on a variation of the Local Binary Pattern (LBP) technique to efficiently work with depth video sequences. Other major consideration is the especial hand sign language used for the UAV control. A tradeoff between the use of natural hand signs and the minimization of the inter-sign interference has been established. Promising results have been achieved in a depth based database of hand gestures especially developed for the validation of the proposed system.