Wind Preview-Based Model Predictive Control of Multi-Rotor UAVs Using LiDAR

Autonomous outdoor operations of Unmanned Aerial Vehicles (UAVs), such as quadrotors, expose the aircraft to wind gusts causing a significant reduction in their position-holding performance. This vulnerability becomes more critical during the automated docking of these vehicles to outdoor charging stations. Utilising real-time wind preview information for the gust rejection control of UAVs has become more feasible due to the advancement of remote wind sensing technology such as LiDAR. This work proposes the use of a wind-preview-based Model Predictive Controller (MPC) to utilise remote wind measurements from a LiDAR for disturbance rejection. Here a ground-based LiDAR unit is used to predict the incoming wind disturbance at the takeoff and landing site of an autonomous quadrotor UAV. This preview information is then utilised by an MPC to provide the optimal compensation over the defined horizon. Simulations were conducted with LiDAR data gathered from field tests to verify the efficacy of the proposed system and to test the robustness of the wind-preview-based control. The results show a favourable improvement in the aircraft response to wind gusts with the addition of wind preview to the MPC; An 80% improvement in its position-holding performance combined with reduced rotational rates and peak rotational angles signifying a less aggressive approach to increased performance when compared with only feedback based MPC disturbance rejection. System robustness tests demonstrated a 1.75 s or 120% margin in the gust preview’s timing or strength respectively before adverse performance impact. The addition of wind-preview to an MPC has been shown to increase the gust rejection of UAVs over standard feedback-based MPC thus enabling their precision landing onto docking stations in the presence of wind gusts

Proceedings of the International Micro Air Vehicles Conference and Flight Competition 2017 (IMAV 2017)

The IMAV 2017 conference has been held at ISAE-SUPAERO, Toulouse, France from Sept. 18 to Sept. 21, 2017. More than 250 participants coming from 30 different countries worldwide have presented their latest research activities in the field of drones. 38 papers have been presented during the conference including various topics such as Aerodynamics, Aeroacoustics, Propulsion, Autopilots, Sensors, Communication systems, Mission planning techniques, Artificial Intelligence, Human-machine cooperation as applied to drones

Onboard Robust Nonlinear Control for Multiple Multirotor UAVs

In this thesis, we focus on developing and validating onboard robust nonlinear control approaches for multiple multirotor unmanned aerial vehicles (UAVs), for the promise of achieving nontrivial tasks, such as path following with aggressive maneuvers, navigation in complex environments with obstacles, and formation in group. To fulfill these challenging missions, the first concern comes with the stability of flight control for the aggressive UAV maneuvers with large tilted angles. In addition, robustness of control is highly desired in order to lead the multirotor UAVs to safe and accurate performance under disturbances. Furthermore, efficiency of control algorithm is a crucial element for the onboard implementation with limited computational capability. Finally, the potential to simultaneously control a group of UAVs in a stable fashion is required. All of these concerns motivate our work in this thesis in the following aspects. We first propose the problem of robust control for the nontrivial maneuvers of a multirotor UAV under disturbances. A complete framework is developed to enable the UAV to achieve the challenging tasks, which consists of a nonlinear attitude controller based on the solution of global output regulation problems for the rigid body rotations SO(3), a backstepping-like position controller, a six-dimensional (6D) wrench observer to estimate the unknown force and torque disturbances, and an online trajectory planner based on a model predictive control (MPC) method. We prove the strong convergence properties of the proposed method both in theory and via intensive real-robot experiments of aggressive waypoint navigation and large-tilted path following tasks in the presence of external disturbances, e.g. wind gusts. Secondly, we propose the problem of autonomous navigation of a multirotor UAV in complex scenarios. We present an effective and robust control approach, namely a fast MPC method with the inclusion of nonlinear obstacle avoiding constraints, and implement it onboard the UAV at 50Hz. The developed approach enables the navigation for a multirotor UAV in 3D environments with multiple obstacles, by autonomously deciding to fly over or around the randomly located obstacles. The third problem that is addressed in our work is formation control for a group of multirotor UAVs. We solve this problem by proposing a distributed formation control algorithm for multiple UAVs based on the solution of retraction balancing problem. The algorithm brings the whole group of UAVs simultaneously to a prescribed submanifold that determines the formation shape in an asymptotically stable fashion in 2D and 3D environments. We validate our proposed algorithm via a series of hardware-in-the-loop simulations and real-robot experiments in various formation cases of arbitrary time-varying (e.g. expanding, shrinking or moving) shapes. In the actual experiments, up to 4 multirotors have been implemented to form arbitrary triangular, rectangular and circular shapes drawn by the operator via a human-robot-interaction device. We have also carried out virtual tests using up to 6 onboard computers to achieve a spherical formation and a formation moving through obstacles.In dieser Arbeit konzentrieren wir uns auf die Entwicklung und Validierung von robusten nichtlinearen On-Bord Steuerungsansatzen für mehrere unbemannte Multirotor-Luftfahrzeuge (UAVs), mit dem Ziel, nicht triviale Aufgaben zu erledigen wie z.B. Wegfolge mit aggressiven Manovern, Navigation in komplexen Umgebungen mit Hindernissen und Formationsflug in einer Gruppe. Um diese anspruchsvollen Missionen zu erfullen liegt unser Hauptaugenmerk bei der Stabilität der Flugsteuerung für aggressive UAV Manöver mit steilen Lagewinkeln. Des weiteren ist Kontroll-robustheit sehr wünschenswert, um die Multirotor-UAVs unter Beeinflussung sicher und genau zu steuern. Daruber hinaus ist die Effizienz des Kontrollalgorithmus ein wichtiges Element für die Onboard-Implementierung mit eingeschrankter Rechenfähigkeit. Abschliessend ist das Potenzial, gleichzeitig eine Gruppe von UAVs in stabiler Weise zu kontrollieren, erforderlich. All dies motiviert uns zur Arbeit an den folgenden Aspekten: Zuerst behandeln wir das Problem der robusten Steuerung nichttrivialer Manöver eines Multirotor UAV unter Störeinfluss. Ein komplettes Framework wird entwickelt, welches dem UAV ermöglicht diese anspruchsvollen Aufgaben zu bewältigen. Es beinhaltet einem nichtlinearen Lageregler, basierend auf der Lösung von globalen Ausgangsrege lungsproblemen für Starrkörperrotationen SO(3), einem backstepping basierten Positionsregler, einen sechsdimensionalen (6D) wrench observer um die unbekannten Kraftund Drehmomenteinflusse zu schätzen, sowie einem Online-Trajektorienplaner basierend auf Model Predictive Control (MPC). Wir weisen die starken Konvergenzcharakteristiken der vorgeschlagenen Methode nach, sowohl in der Theorie als auchmittels intensiver Real-roboter-Experimente, mit aggressiver Wegpunktnavigation und Wegfindungsaufgaben in extremer Fluglage in Gegenwart externer Einflüsse, z.B. Windböen. Als nächstes bearbeiten wir das Problem der autonomen Navigation eines Multirotor UAV in komplexen Szenarien. Wir stellen einen effektiven und robusten Steuerungsansatz dar, nämlich eine schnelle MPC-Methode mit der Einbeziehung von nichtlinearer Einschränkungen zur Hindernisvermeidung, und implmenetieren diese an Bord des UAV mit 50Hz. Der entwickelte Ansatz ermöglicht die Navigation eines Multirotor UAVs in 3D-Umgebungen mit mehreren Hindernissen, wobei autonom entschieden wir, über oder um die zufällig gelegenen Hindernisse zu fliegen. Das dritte Problem, das in unserer Arbeit angesprochen wird, ist die Bildungssteuerung für eine Gruppe von Multirotor UAVs. Wir lösen dieses Problem, indem wir einen verteilten Formationskontrollalgorithmus für mehrere UAVs auf der Grundlage der Lösung des Retraction Balancing Problems vorschlagen. Der Algorithmus bringt die ganze Gruppe von UAVs gleichzeitig auf eine vorgeschriebene Untermanigfaltigkeit, welche die Formation in asymtotisch stabiler Weise in 2D- und 3D-Umgebungen bestimmt. Wir validieren unseren vorgeschlagenen Algorithmus uber eine Reihe von Hardware-in-the- ¨ Loop-Simulationen und Real-Roboter-Experimente mit verschiedenen Formationsvarianten in beliebigen zeitveränderlichen (z. B. expandierenden, schrumpfenden oder bewegten) Formen. In den eigentlichen Experimenten wurden bis zu 4 Multirotoren eingesetzt, um beliebige dreieckige, rechteckige und kreisförmige Formen zu bilden, die vom Bediener über eine Mensch-Roboter-Interaktionsvorrichtung vorgezeichnet wurden. Wir haben auch virtuelle Tests mit bis zu 6 Onboard-Computern durchgeführt, um eine sphärische Formation und eine Formation zu erreichen, die sich durch Hindernisse. bewegt

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 matemàtic 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 vehículos 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 vehículo para que siga una trayectoria predefinida mientras evita los obstáculos que puedan aparecer en el recorrido del vehículo. 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 vehículo 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 vehículo 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 vehículo al instante si ocurre algún evento inesperado, como lo es la detección de un obstáculo en el recorrido del vehículo. 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 vehículo.Postprint (published version

A Water-Surface Self-Leveling Landing Platform for Small-Scale UAVs

Because many of the most widely used UAVs, such as the Vertical Take-Off and Landing (VTOL), cannot land securely on sloped or fast-changing surfaces, there is a need to design better deployment and landing stations. This document proposes an approach to design a water-surface self-leveling landing platform by implementing the best concept to be used as a safe ground for UAVs to land and deploy on open waters. After conceptualizing multiple design ideas, these options were laid out in a decision matrix with four criteria: degrees of freedom, mechanical complexity, manufacturing, and cost. The chosen concept was the spherical parallel manipulator that provides the most degrees of freedom and design symmetry allowing for an easy manufacturing process and better control precision. This concept proves innovative as it improves the range of motion with lower energy requirements resulting in a device that provides low inertia, high velocity, and precise spherical motion [36]. A spherical parallel manipulator platform was designed in SolidWorks, and then a 3D-printed prototype was assembled and tested. The forward and inverse kinematic of the mechanism were thoroughly analyzed, and tests were performed to verify the ideal inverse kinematic solution