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On the Application of Rotating Cylinders to Micro Air Vehicles
An investigation into the feasibility of using rotating circular cylinders as the primary means of generating lift for the class of very small (0.15 m maximum dimension, 50 g weight) unmanned aircraft known as Micro Air Vehicles (MAV) has been carried out. It is hoped that such a design would be able to exploit the large lift generating properties of the
rotating cylinder for the purposes of increasing the available payload weight. This would provide considerable benefits as, at present, the inability to support capable payloads significantly restricts the usefulness of MAV-sized craft.
A preliminary design study was performed to investigate possible configurations for the proposed design, resulting in the selection, for reasons of simplicity, of an arrangement having two rotating cylinders about a central fuselage. Initial assessments of the practical feasibility of such a design, as well as its likely performance (in terms of lift, drag, and power requirements) were then carried out. An examination of the consequences of the presence of the cylinders on the stability and control of such a vehicle was also performed. Existing understanding of the aerodynamic characteristics of a rotating cylinder in crossflow was extended through a series of wind tunnel tests examining all aspects of rotating cylinder flow, including force and moment coefficients, behaviour at non-zero yaw angles (−30� � � 10�), power requirements for spinning the cylinder, and wake phenomena. A particular focus was the use of endplates to improve aerodynamic performance. The tests were conducted with a cylinder of aspect ratio AR = 5 across a range of Reynolds numbers (1.6 × 104 � Re � 9.5 × 104, based on cylinder diameter) and velocity ratios (4) identified as being of interest by the preliminary design study. The results were generally found to be in very good agreement with existing published data, though power requirements for spinning the cylinder were much higher than anticipated, and revealed the influence of tip vortices to be of great significance. Wind tunnel experiments with a simple prototype aircraft, based on the outcome of the preliminary design study and isolated cylinder tests, examined the overall aerodynamic performance of this type of design for a single Reynolds number of Re = 1.8×104, across a velocity ratio range of 2.5, and at various angles of attack (−10� 25�) and yaw (−10� 30�). These tests also investigated the interaction between the cylinders and the other components of the aircraft to help determine the most favourable layout. The tests revealed the effect of propeller wash over the rotors, the influence of the cylinder wake on the tail, and the design of the tail, fin, and fuselage to be of considerable importance to the aerodynamic characteristics and performance of the vehicle. Overall, the study indicated that an aircraft of the proposed configuration and suitable capability was theoretically possible at the MAV scale of flight if an appropriate rotor geometry was chosen. However, the actual construction of a vehicle able to fully provide the desired performance within the constraints placed on platform size and weight was not currently possible using commonly available materials and components. Slightly larger designs (of dimension 0.4 m and weight 250 g) were more realisable, but still lacked in performance. Successful development of this type of design is thus dependent on technological advancement, particularly improvements in power and propulsion systems
Guidance, navigation and control of multirotors
Aplicat embargament des de la data de defensa fins el dia 31 de desembre de 2021This thesis presents contributions to the Guidance, Navigation and Control (GNC) systems for multirotor vehicles by applying and developing diverse control techniques and machine learning theory with innovative results. The aim of the thesis is to obtain a GNC system able to make the vehicle follow predefined paths while avoiding obstacles in the vehicle's route. The system must be adaptable to different paths, situations and missions, reducing the tuning effort and parametrisation of the proposed approaches. The multirotor platform, formed by the Asctec Hummingbird quadrotor vehicle, is studied and described in detail. A complete mathematical model is obtained and a freely available and open simulation platform is built. Furthermore, an autopilot controller is
designed and implemented in the real platform. The control part is focused on the path following problem. That is, following a predefined path in space without any time constraint. Diverse control-oriented and geometrical algorithms are studied, implemented and compared. Then, the geometrical algorithms are improved by obtaining adaptive approaches that do not need any parameter tuning. The adaptive geometrical approaches are developed by means of Neural Networks. To end up, a deep reinforcement learning approach is developed to solve the path following problem. This approach implements the Deep Deterministic Policy Gradient algorithm. The resulting approach is trained in a realistic multirotor simulator and tested in real experiments with success. The proposed approach is able to accurately follow a path while adapting the vehicle's velocity depending on the path's shape. In the navigation part, an obstacle detection system based on the use of a LIDAR sensor is implemented. A model of the sensor is derived and included in the simulator. Moreover, an approach for treating the sensor data to eliminate the possible ground detections is developed. The guidance part is focused on the reactive path planning problem. That is, a path planning algorithm that is able to re-plan the trajectory online if an unexpected event, such as detecting an obstacle in the vehicle's route, occurs. A deep reinforcement learning approach for the reactive obstacle avoidance problem is developed. This approach implements the Deep Deterministic Policy Gradient algorithm. The developed deep reinforcement learning agent is trained and tested in the realistic simulation platform. This agent is combined with the path following agent and the rest of the elements developed in the thesis obtaining a GNC system that is able to follow different types of paths while avoiding obstacle in the vehicle's route.Aquesta tesi doctoral presenta diverses contribucions relaciones amb els sistemes de Guiat, Navegació i Control (GNC) per a vehicles multirrotor, aplicant i desenvolupant diverses tècniques de control i de machine learning amb resultats innovadors. L'objectiu principal de la tesi és obtenir un sistema de GNC capaç de dirigir el vehicle perquè segueixi una trajectòria predefinida mentre evita els obstacles que puguin aparèixer en el recorregut del vehicle. El sistema ha de ser adaptable a diferents trajectòries, situacions i missions, reduint l'esforç realitzat en l'ajust i la parametrització dels mètodes proposats. La plataforma experimental, formada pel cuadricòpter Asctec Hummingbird, s'estudia i es descriu en detall. S'obté un model matemà tic complet de la plataforma i es desenvolupa una eina de simulació, la qual és de codi lliure. A més, es dissenya un controlador autopilot i s'implementa en la plataforma real. La part de control està enfocada al problema de path following. En aquest problema, el vehicle ha de seguir una trajectòria predefinida en l'espai sense cap tipus de restricció temporal. S'estudien, s'implementen i es comparen diversos algoritmes de control i geomètrics de path following. Després, es milloren els algoritmes geomètrics usant xarxes neuronals per convertirlos en algoritmes adaptatius. Per finalitzar, es desenvolupa un mètode de path following basat en tècniques d'aprenentatge per reforç profund (deep Reinforcement learning). Aquest mètode implementa l'algoritme Deep Deterministic Policy Gradient. L'agent intel. ligent resultant és entrenat en un simulador realista de multirotors i validat en la plataforma experimental real amb èxit. Els resultats mostren que l'agent és capaç de seguir de forma precisa la trajectòria de referència adaptant la velocitat del vehicle segons la curvatura del recorregut. A la part de navegació, s'implementa un sistema de detecció d'obstacles basat en l'ús d'un sensor LIDAR. Es deriva un model del sensor i aquest s'inclou en el simulador. A més, es desenvolupa un mètode per tractar les mesures del sensor per eliminar les possibles deteccions del terra. Pel que fa a la part de guiatge, aquesta està focalitzada en el problema de reactive path planning. És a dir, un algoritme de planificació de trajectòria que és capaç de re-planejar el recorregut del vehicle a l'instant si algun esdeveniment inesperat ocorre, com ho és la detecció d'un obstacle en el recorregut del vehicle. Es desenvolupa un mètode basat en aprenentatge per reforç profund per l'evasió d'obstacles. Aquest mètode implementa l'algoritme Deep Deterministic Policy Gradient. L'agent d'aprenentatge per reforç s'entrena i valida en un simulador de multirotors realista. Aquest agent es combina amb l'agent de path following i la resta d'elements desenvolupats en la tesi per obtenir un sistema GNC capaç de seguir diferents tipus de trajectòries, evadint els obstacles que estiguin en el recorregut del vehicle.Esta tesis doctoral presenta varias contribuciones relacionas con los sistemas de Guiado, Navegación y Control (GNC) para vehÃculos multirotor, aplicando y desarrollando diversas técnicas de control y de machine learning con resultados innovadores. El objetivo principal de la tesis es obtener un sistema de GNC capaz de dirigir el vehÃculo para que siga una trayectoria predefinida mientras evita los obstáculos que puedan aparecer en el recorrido del vehÃculo. El sistema debe ser adaptable a diferentes trayectorias, situaciones y misiones, reduciendo el esfuerzo realizado en el ajuste y la parametrización de los métodos propuestos.
La plataforma experimental, formada por el cuadricoptero Asctec Hummingbird, se estudia y describe en detalle. Se obtiene un modelo matemático completo de la plataforma y se desarrolla una herramienta de simulación, la cual es de código libre. Además, se diseña un controlador autopilot, el cual es implementado en la plataforma real.
La parte de control está enfocada en el problema de path following. En este problema, el vehÃculo debe seguir una trayectoria predefinida en el espacio tridimensional sin ninguna restricción temporal
Se estudian, implementan y comparan varios algoritmos de control y geométricos de path following. Luego, se mejoran los algoritmos geométricos usando redes neuronales para convertirlos en algoritmos adaptativos. Para finalizar, se desarrolla un método de path following basado en técnicas de aprendizaje por refuerzo profundo (deep reinforcement learning). Este método
implementa el algoritmo Deep Deterministic Policy Gradient. El agente inteligente resultante es entrenado en un simulador realista de multirotores y validado en la plataforma experimental real con éxito. Los resultados muestran que el agente es capaz de seguir de forma precisa la trayectoria de referencia adaptando la velocidad del vehÃculo según la curvatura del recorrido.
En la parte de navegación se implementa un sistema de detección de obstáculos basado en el uso de un sensor LIDAR. Se deriva un modelo del sensor y este se incluye en el simulador. Además, se desarrolla un método para tratar las medidas del sensor para eliminar las posibles detecciones
del suelo.
En cuanto a la parte de guiado, está focalizada en el problema de reactive path planning. Es decir, un algoritmo de planificación de trayectoria que es capaz de re-planear el recorrido del vehÃculo al instante si ocurre algún evento inesperado, como lo es la detección de un obstáculo en el recorrido del vehÃculo. Se desarrolla un método basado en aprendizaje por refuerzo profundo para la evasión de obstáculos. Este implementa el algoritmo Deep Deterministic Policy Gradient.
El agente de aprendizaje por refuerzo se entrena y valida en un simulador de multirotors realista.
Este agente se combina con el agente de path following y el resto de elementos desarrollados en la tesis para obtener un sistema GNC capaz de seguir diferentes tipos de trayectorias evadiendo los obstáculos que estén en el recorrido del vehÃculo.Postprint (published version