The MRS UAV System: Pushing the Frontiers of Reproducible Research, Real-world Deployment, and Education with Autonomous Unmanned Aerial Vehicles

We present a multirotor Unmanned Aerial Vehicle control (UAV) and estimation system for supporting replicable research through realistic simulations and real-world experiments. We propose a unique multi-frame localization paradigm for estimating the states of a UAV in various frames of reference using multiple sensors simultaneously. The system enables complex missions in GNSS and GNSS-denied environments, including outdoor-indoor transitions and the execution of redundant estimators for backing up unreliable localization sources. Two feedback control designs are presented: one for precise and aggressive maneuvers, and the other for stable and smooth flight with a noisy state estimate. The proposed control and estimation pipeline are constructed without using the Euler/Tait-Bryan angle representation of orientation in 3D. Instead, we rely on rotation matrices and a novel heading-based convention to represent the one free rotational degree-of-freedom in 3D of a standard multirotor helicopter. We provide an actively maintained and well-documented open-source implementation, including realistic simulation of UAV, sensors, and localization systems. The proposed system is the product of years of applied research on multi-robot systems, aerial swarms, aerial manipulation, motion planning, and remote sensing. All our results have been supported by real-world system deployment that shaped the system into the form presented here. In addition, the system was utilized during the participation of our team from the CTU in Prague in the prestigious MBZIRC 2017 and 2020 robotics competitions, and also in the DARPA SubT challenge. Each time, our team was able to secure top places among the best competitors from all over the world. On each occasion, the challenges has motivated the team to improve the system and to gain a great amount of high-quality experience within tight deadlines.Comment: 28 pages, 20 figures, submitted to Journal of Intelligent & Robotic Systems (JINT), for the provided open-source software see http://github.com/ctu-mr

UAV or Drones for Remote Sensing Applications in GPS/GNSS Enabled and GPS/GNSS Denied Environments

The design of novel UAV systems and the use of UAV platforms integrated with robotic sensing and imaging techniques, as well as the development of processing workflows and the capacity of ultra-high temporal and spatial resolution data, have enabled a rapid uptake of UAVs and drones across several industries and application domains.This book provides a forum for high-quality peer-reviewed papers that broaden awareness and understanding of single- and multiple-UAV developments for remote sensing applications, and associated developments in sensor technology, data processing and communications, and UAV system design and sensing capabilities in GPS-enabled and, more broadly, Global Navigation Satellite System (GNSS)-enabled and GPS/GNSS-denied environments.Contributions include:UAV-based photogrammetry, laser scanning, multispectral imaging, hyperspectral imaging, and thermal imaging;UAV sensor applications; spatial ecology; pest detection; reef; forestry; volcanology; precision agriculture wildlife species tracking; search and rescue; target tracking; atmosphere monitoring; chemical, biological, and natural disaster phenomena; fire prevention, flood prevention; volcanic monitoring; pollution monitoring; microclimates; and land use;Wildlife and target detection and recognition from UAV imagery using deep learning and machine learning techniques;UAV-based change detection

WATER-BASED MITIGATION TECHNIQUES AND NETWORK INTEGRATION TO COUNTER DRONE SWARMS

Potential and current U.S. adversaries are purchasing and deploying commercial small Unmanned Aircraft Systems (sUAS) in networked swarms. These swarms can be used for intelligence collection and reconnaissance, and have the potential to be weaponized as well. Additionally, the unlawful, but probably not malicious, activity of civilian UAS (drone) operators is of increasing concern. More specifically, there is increased risk to naval assets while in constrained environments, such as harbor transit, where both navigation and weaponized responses are serious concerns. This thesis uses the scenario of protecting a U.S. Navy destroyer entering and exiting a harbor to develop a sUAS mitigation procedure based on existing firefighting and counter-piracy technologies. The proposed procedure includes a communications plan and can be implemented almost immediately using existing civilian and military assets. Additional recommendations to improve the performance of such procedures are provided.CRUSARRRTOLieutenant, United States NavyApproved for public release. Distribution is unlimited

Methodology for precision landing of unmanned aerial vehicles on a mobile base

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáThe integration of heterogeneous robotic systems is a constant topic today as a promising strategy to overcome the inherent limitations of each system. With this in view, this study explores the development of a precision landing system for Unmanned Aerial Vehicles (UAVs), designed to land autonomously on static and moving targets. To achieve this, a detailed analysis of aspects of the system is first carried out, such as the definition of the fiducial marker, the control architecture, and the definition of gains, followed by the development of the code, which includes the architecture and the interface with an operator. After development, tests begin which are divided into two stages: the first verifies the UAV’s ability to identify and follow moving targets, and the second consists of precision landing experiments in different scenarios. The results of the investigation indicate that the combination of a complete PID controller with Aruco markers is more effective, which is why they were selected for the development of the system. Tracking tests have proven the driver’s ability to guide the UAV to autonomously follow a UGV, although it presents difficulties with high angular speeds. On the other hand, autonomous landing tests showed high efficiency in constant speed scenarios but revealed some failures in situations with sudden changes and requests to the rotation driver.A integração de sistemas robóticos heterogêneos é um tópico constante atualmente como uma estratégia promissora para ultrapassar as limitações inerentes a cada sistema individualmente. Com isso, este estudo explora o desenvolvimento de um sistema de pouso de precisão para Veículos Aéreos Não Tripulados (UAVs), destinado a aterragens em alvos estáticos e em movimento autonomamente. Para isso, primeiro é feita uma análise detalhada de aspectos do sistema, como a definição do marcador fiducial, da arquitetura de controle e definição de ganhos, seguido do desenvolvimento do código, que inclui a arquitetura e a interface com o operador. Após o desenvolvimento, inicia-se os testes que se dividem em duas etapas: a primeira verifica a capacidade do UAV de identificar e seguir alvos em movimento, e a segunda consiste em experimentos de pouso de precisão em diversos cenários. Os resultados da investigação indicam que a combinação de um controlador PID completo com marcadores Aruco é mais eficaz, razão pela qual foram selecionados para o desenvolvimento do sistema. Os testes de rastreamento comprovaram a habilidade do controlador em orientar o UAV para seguir autonomamente um UGV, embora apresente dificuldades com velocidades angulares elevadas. Por outro lado, os testes de pouso autônomo mostraram alta eficiência em cenários de velocidade constante, mas revelaram algumas falhas em situações com mudanças bruscas e desafiadoras para o controlador de rotação

Unmanned Aerial Systems: Research, Development, Education & Training at Embry-Riddle Aeronautical University

With technological breakthroughs in miniaturized aircraft-related components, including but not limited to communications, computer systems and sensors, state-of-the-art unmanned aerial systems (UAS) have become a reality. This fast-growing industry is anticipating and responding to a myriad of societal applications that will provide new and more cost-effective solutions that previous technologies could not, or will replace activities that involved humans in flight with associated risks. Embry-Riddle Aeronautical University has a long history of aviation-related research and education, and is heavily engaged in UAS activities. This document provides a summary of these activities, and is divided into two parts. The first part provides a brief summary of each of the various activities, while the second part lists the faculty associated with those activities. Within the first part of this document we have separated UAS activities into two broad areas: Engineering and Applications. Each of these broad areas is then further broken down into six sub-areas, which are listed in the Table of Contents. The second part lists the faculty, sorted by campus (Daytona Beach-D, Prescott-P and Worldwide-W) associated with the UAS activities. The UAS activities and the corresponding faculty are cross-referenced. We have chosen to provide very short summaries of the UAS activities rather than lengthy descriptions. If more information is desired, please contact me directly, or visit our research website (https://erau.edu/research), or contact the appropriate faculty member using their e-mail address provided at the end of this document

A review of aerial manipulation of small-scale rotorcraft unmanned robotic systems

Small-scale rotorcraft unmanned robotic systems (SRURSs) are a kind of unmanned rotorcraft with manipulating devices. This review aims to provide an overview on aerial manipulation of SRURSs nowadays and promote relative research in the future. In the past decade, aerial manipulation of SRURSs has attracted the interest of researchers globally. This paper provides a literature review of the last 10 years (2008–2017) on SRURSs, and details achievements and challenges. Firstly, the definition, current state, development, classification, and challenges of SRURSs are introduced. Then, related papers are organized into two topical categories: mechanical structure design, and modeling and control. Following this, research groups involved in SRURS research and their major achievements are summarized and classified in the form of tables. The research groups are introduced in detail from seven parts. Finally, trends and challenges are compiled and presented to serve as a resource for researchers interested in aerial manipulation of SRURSs. The problem, trends, and challenges are described from three aspects. Conclusions of the paper are presented, and the future of SRURSs is discussed to enable further research interests

Design, Deployment, Navigation, and Control of Mobile Robots for Perception and Sensor Data Collection

Aerial robots, including rotary-wing and fixed-wing unmanned aerial vehicles or UAVs, have shown great capabilities in surveying as well as search and rescue from above. However, either rotary-wing or fixed-wing UAVs have nearly insoluble flaws. In order to overcome the under-actuating nature of multi-rotor UAVs, Chapter 2 proposes modeling methods and control schemes for fully-actuated hexacopters. Additionally, rotary-wing robots suffer from limited battery life as well as lack of fail-safe mechanism upon losing motors, while fixed-wing robots lacks the ability to take off and land vertically. Therefore, Chapter 4 proposes a bio-inpired hybrid aerial robot to extend mutli-rotor flight time and fail-safe capability and provide fixed-wing glider with vertical take-off and landing or VTOL capability. Moreover, to extend the flight time and optimize the energy consumption of multi-rotor UAVs, Chapter 3 proposes a multi-disciplinary design optimization based flight trajectory optimizer involving linear rotor inflow models to reduce flight time or energy consumption of specific missions.In terms of unmanned ground vehicles or UGVs used for perception and mapping, there has been a research gap to provide a low-cost, highly agile over-actuated chassis design. Chapter 5 proposes a 3D-printable double Ackermann steering chassis design with 2-wheel standing and balancing capability to fill in this gap. Chapter 6, on the other hand, proposes the system design of a UGV capable of performing perception and mapping in a limited lighting, unstructured, and GPS-denied environment based on a nevertheless nonholonomic chassis, where primary concern becomes the reliability in performing real-time mapping and preservation of solely static environment.The last but not least topic discussed in this dissertation is to promote the role of UAV imagery in earthquake response. In Chapter 7 we combine the traditional UAV plan view perspective with north and east elevation view video data to provide motion estimation in all 6 degrees of freedom, as well as proposing Video Transformer for motion tracking.All in all, with attempts to expand and promote the designs, deployment and control schemes of both aerial and ground mobile robots, this dissertation strives to provide case study results and state-of-the-art methods for future robotics studies

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