Event-based control strategy for consensus of a group of VTOL-UAVs

International audienceThis paper presents the development of a collaborative event-based control applied to the problem of consensus and formation of a group of VTOL-UAVs (Vertical Takeoff and Landing, Unmanned Aerial Vehicles). Each VTOL-UAV decides, based on the difference of its current state (linear position and velocity) and its latest broadcast state, when it has to send a new value to its neighbors. The asymptotic convergence to average consensus or desired formation is depicted via numerical simulations

Distributed management and coordination of UAV swarms based on infrastructureless wireless networks

[ES] Los Vehículos Aéreos no Tripulados (o drones) ya han demostrado su utilidad en una gran variedad de aplicaciones. Hoy en día, se utilizan para fotografía, cinematografía, inspecciones y vigilancia, entre otros. Sin embargo, en la mayoría de los casos todavía son controlados por un piloto, que como máximo suele estar volando un solo dron cada vez. En esta tesis, tratamos de avanzar en paso más allá en esta tecnología al permitir que múltiples drones con capacidad para despegue y aterrizaje vertical trabajen de forma sincronizada, como una sola entidad. La principal ventaja de realizar vuelos en grupo, comúnmente denominado enjambre, es que se pueden realizar tareas más complejas que utilizando un solo dron. De hecho, un enjambre permite cubrir más área en el mismo tiempo, ser más resistente, tener una capacidad de carga más alta, etc. Esto puede habilitar el uso de nuevas aplicaciones, o una mejor eficiencia para las aplicaciones existentes. Sin embargo, una parte clave es que los miembros del enjambre deben organizarse correctamente, ya que, durante el vuelo, diferentes perturbaciones pueden provocar que sea complicado mantener el enjambre como una unidad coherente. Una vez que se pierde esta coherencia, todos los beneficios previamente mencionados de un enjambre se pierden también. Incluso, aumenta el riesgo de colisiones entre los elementos del enjambre. Por lo tanto, esta tesis se centra en resolver algunos de estos problemas, proporcionando un conjunto de algoritmos que permitan a otros desarrolladores crear aplicaciones de enjambres de drones. Para desarrollar los algoritmos propuestos hemos incorporado mejoras al llamado ArduSim. Este simulador nos permite simular tanto la física de un dron como la comunicación entre drones con un alto grado de precisión. ArduSim nos permite implementar protocolos y algoritmos (bien probados) en drones reales con facilidad. Durante toda la tesis, ArduSim ha sido utilizado ampliamente. Su utilización ha permitido que las pruebas fueran seguras, y al mismo tiempo nos permitió ahorrar mucho tiempo, dinero y esfuerzo de investigación. Comenzamos nuestra investigación sobre enjambres asignando posiciones aéreas para cada dron en el suelo. Suponiendo que los drones están ubicados aleatoriamente en el suelo, y que necesitan alcanzar una formación aérea deseada, buscamos una solución que minimice la distancia total recorrida por todos los drones. Para ello se empezó con un método de fuerza bruta, pero rápidamente nos dimos cuenta de que, dada su alta complejidad, este método funciona mal cuando el número de drones aumenta. Por lo tanto, propusimos una heurística. Como en todas las heurísticas, se realizó un compromiso entre complejidad y precisión. Al simplificar el problema, encontramos que nuestra heurística era capaz de calcular una solución muy rápidamente sin aumentar sustancialmente la distancia total recorrida. Además, implementamos el algoritmo de Kuhn-Munkres (KMA), un algoritmo que ha demostrado proporcionar la respuesta exacta (es decir, reducir la distancia total recorrida) en el menor tiempo posible. Después de muchos experimentos, llegamos a la conclusión de que nuestra heurística es más rápida, pero que la solución proporcionada por el KMA es ligeramente más eficiente. En particular, aunque la diferencia en la distancia total recorrida es pequeña, el uso de KMA reduce el número de trayectorias de vuelo que se cruzan entre sí, lo cual es una métrica importante para las siguientes propuestas.[...][CA] Els vehicles aeris no tripulats (o drons) ja han demostrat la seua utilitat en una gran varietat d'aplicacions. Avui dia, s'utilitzen per a fotografia, cinematografia, inspeccions i vigilància, entre altres. No obstant això, en la majoria dels casos encara són controlats per un pilot, que com a màxim sol controlar el vol d'un sol dron cada vegada. En aquesta tesi, tractem d'avançar un pas més enllà en aquesta tecnologia, en permetre que múltiples drons amb capacitat per a l'enlairament i l'aterratge vertical treballen de forma sincronitzada, com una sola entitat. El principal avantatge de realitzar vols en grup, comunament denominats eixam, és que es poden fer tasques més complexes que utilitzant un sol dron. De fet, un eixam permet cobrir més àrea en el mateix temps, ser més resistent, tenir una capacitat de càrrega més alta, etc. Això pot habilitar l'ús de noves aplicacions, o una millor eficiència per a les aplicacions existents. No obstant això, una punt clau és que els membres de l'eixam han d'organitzar-se correctament, ja que, durant el vol, diferents pertorbacions poden provocar que siga complicat mantenir l'eixam com una unitat coherent. Una vegada que es perd aquesta coherència, tots els beneficis prèviament esmentats d'un eixam es perden també. Fins i tot, augmenta el risc de col·lisions entre els elements de l'eixam. Per tant, aquesta tesi se centra a resoldre alguns d'aquests problemes, proporcionant un conjunt d'algorismes que permeten a altres desenvolupadors crear aplicacions d'eixams de drons. Per a desenvolupar els algorismes proposats hem incorporat millores a l'anomenat ArduSim. Aquest simulador ens permet simular tant la física d'un dron com la comunicació entre drons amb un alt grau de precisió. ArduSim ens permet implementar protocols i algorismes (ben provats) en drons reals amb facilitat. Durant tota la tesi, ArduSim s'ha utilitzat àmpliament. El seu ús ha permès que les proves foren segures, i al mateix temps ens va permetre estalviar molt de temps, diners i esforç d'investigació. Per tant, es va utilitzar ArduSim per a cada bloc de construcció que vam desenvolupar. Comencem la nostra recerca sobre eixams assignant posicions aèries per a cada dron en terra. Suposant que els drons estan situats aleatòriament en terra i que necessiten assolir la formació aèria desitjada, cerquem una solució que minimitze la distància total recorreguda per tots els drons. Per a això, es va començar amb un mètode de força bruta, però ràpidament ens vam adonar que, atesa l'alta complexitat, aquest mètode funciona malament quan el nombre de drons augmenta. Per tant, vam proposar una heurística. Com en totes les heurístiques, es va fer un compromís entre complexitat i precisió. En simplificar el problema, trobem que la nostra heurística era capaç de calcular una solució molt ràpidament sense augmentar substancialment la distància total recorreguda. A més, vam implementar l'algorisme de Kuhn-Munkres (KMA), un algorisme que ha demostrat proporcionar la resposta exacta (és a dir, reduir la distància total recorreguda) en el menor temps possible. Després de molts experiments, arribem a la conclusió que la nostra heurística és més ràpida, però que la solució proporcionada pel KMA és lleugerament més eficient. En particular, encara que la diferència en la distància total recorreguda és xicoteta, l'ús de KMA redueix el nombre de trajectòries de vol que s'encreuen entre si, la qual cosa és una mètrica important per a les propostes següents.[...][EN] Unmanned Aerial Vehicles (UAVs) have already proven to be useful in many different applications. Nowadays, they are used for photography, cinematography, inspections, and surveillance. However, in most cases they are still controlled by a pilot, who at most is flying one UAV at a time. In this thesis, we try to take this technology one step further by allowing multiple Vertical Take-off and Landing (VTOL) UAVs to work together as one entity. The main advantage of this group, commonly referred to as a swarm, is that it can perform more complex tasks than a single UAV. When organized correctly, a swarm allows for: more area to be covered in the same time, more resilience, higher load capability, etc. A swarm can lead to new applications, or a better efficiency for existing applications. A key part, however, is that they should be organized correctly. During the flight, different disturbances will make it complicated to keep the swarm as one coherent unit. Once this coherency is lost, all the previously mentioned benefits of a swarm are lost as well. Even worse, the chance of a hazard increases. Therefore, this thesis focuses on solving some of these issues by providing a baseline of building blocks that enable other developers to create UAV swarm applications. In order to develop these building blocks, we improve a multi-UAV simulator called ArduSim. This simulator allows us to simulate both the physics of a UAV, and the communication between UAVs with a high degree of accuracy. This is a crucial part because it allows us to deploy (well tested) protocols and algorithms on real UAVs with ease. During the entirety of this thesis, ArduSim has been used extensively. It made testing safe, and allowed us to save a lot of time, money and research effort. We started by assigning airborne positions for each UAV on the ground. Assuming that the UAVs, are placed randomly on the ground, and that they need to reach a desired aerial formation, we searched for a solution that minimizes the total distance travelled by all the UAVs. We started with a brute-force method, but quickly realized that, given its high complexity, this method performs badly when the number of UAVs grows. Hence, we created a heuristic. As for all heuristics, a trade-off was made between complexity and accuracy. By simplifying the problem, we found that our heuristic was able to calculate a solution very quickly without increasing the total distance travelled substantially. Furthermore, we implemented the \ac{KMA}, an algorithm that has been proven to provide the exact answer (i.e. minimal total distance travelled) in the shortest time possible. After many experiments, we came to the conclusion that our heuristic is faster, but that the solution provided by the \ac{KMA} is slightly better. In particular, although the difference in total distance travelled is small, the \ac{KMA} reduces the numbers of flight paths crossing each other, which is an important metric in our next building block. Once we developed algorithms to assign airborne positions to each UAV on the ground, we started developing algorithms to take off all those UAVs. The objective of these algorithms is to reduce the time it takes for all the UAVs to reach their aerial position, while ensuring that all UAVs maintain a safe distance. The easiest solution is a sequential take-off procedure, but this is also the slowest approach. Hence, we improved it by first proposing a semi-sequential and later a semi-simultaneous take-off procedure. With this semi-simultaneous take-off procedure, we are able to reduce the takeoff time drastically without introducing any risk to the aircraft. [..]Wubben, J. (2023). Distributed management and coordination of UAV swarms based on infrastructureless wireless networks [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19888

Autonomisen multikopteriparven hallinta etsintä- ja pelastustehtävissä

This thesis presents the requirements and implementation of a Ground Control Station (GCS) application for controlling a fleet of multicopters to perform a Search And Rescue (SAR) mission. The requirements are put together by analysing existing drone types, SAR practices, and available GCS applications. Multicopters are found to be the most feasible drone to use for the SAR use case because of their maneuverability, despite not having the best endurance. Several existing area coverage methods are presented and their usefulness is analyzed for SAR scenarios where different amounts of prior knowledge is available. It is stated that most search patterns can be used with a fleet of drones, by creating drone formations and by dividing the target area into sub-areas. It is noted that most currently available GCS applications are focused on controlling a single drone for either industrial or hobby use. A proof of concept prototype is developed on top of an open source GCS and tested in field tests. Based on all the previous learnings from the protype and research, a new GCS is designed and developed. The development on optimizing communications between the GCS and the autopilot leads to a filed patent application. The new software is tested with three multicopters in a water rescue scenario and several user interface improvements are made as a result of the learnings. The development of a GCS for controlling a drone fleet for search and rescue is proven feasible.Työssä esitetään multikopteriparven hallintaan käytettävän Ground Control Station (GCS) ohjelmiston vaatimukset ja toteutus Search And Rescue (SAR) etsintä- ja pelastustehtävien suorittamiseksi. Vaatimukset kootaan yhteen analysoimalla saatavilla olevia droonityyppejä, SAR pelastuskäytäntöjä, sekä GCS ohjelmistoja. Multikopterit osoittautuvat liikkuvuutensa ansiosta pelastustehtäviin sopivimmaksi vaihtoehdoksi, vaikka niiden saavutettavissa oleva lentoaika ei ole parhaimmasta päästä. Erilaisia etsintämetodeja esitetään alueiden kattamiseksi ja niiden hyödyllisyyttä analysoidaan SAR tilanteissa, joissa ennakkotietoa on saatavilla vaihtelevasti. Osoitetaan, että useimpia etsintäalgoritmeja voidaan hyödyntää drooniparvella, muodostamalla lentomuodostelmia, sekä jakamalla kohdealue pienempiin osa-alueisiin. Huomataan, että suurin osa tällä hetkellä saatavilla olevista GCS ohjelmistoista on suunnattu teollisuuden tai harrastelijoiden käyttöön, pääasiassa yksittäisen droonin hallintaan. Prototyyppi kehitetään avoimen lähdekoodin GCS ohjelmiston pohjalta ja testataan kenttätesteissä. Tästä saadun tiedon avulla suunnitellaan ja kehitetään uusi GCS ohjelmisto. Kehitystyö viestinnän optimoinniksi autopilotin ja GCS ohjelmiston välillä johtaa patenttihakemukseen. Uusi ohjelmisto testataan kolmella multikopterilla vesipelastustilanteessa ja sen seurauksena käyttöliittymään tehdään useita parannuksia. GCS ohjelmiston luominen drooniparven hallintaan etsintä- ja pelastustehtävissä todetaan mahdolliseksi

Small unmanned airborne systems to support oil and gas pipeline monitoring and mapping

Acknowledgments We thank Johan Havelaar, Aeryon Labs Inc., AeronVironment Inc. and Aeronautics Inc. for kindly permitting the use of materials in Fig. 1.Peer reviewedPublisher PD

Key technologies for safe and autonomous drones

Drones/UAVs are able to perform air operations that are very difficult to be performed by manned aircrafts. In addition, drones' usage brings significant economic savings and environmental benefits, while reducing risks to human life. In this paper, we present key technologies that enable development of drone systems. The technologies are identified based on the usages of drones (driven by COMP4DRONES project use cases). These technologies are grouped into four categories: U-space capabilities, system functions, payloads, and tools. Also, we present the contributions of the COMP4DRONES project to improve existing technologies. These contributions aim to ease drones’ customization, and enable their safe operation.This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 826610. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Spain, Austria, Belgium, Czech Republic, France, Italy, Latvia, Netherlands. The total project budget is 28,590,748.75 EUR (excluding ESIF partners), while the requested grant is 7,983,731.61 EUR to ECSEL JU, and 8,874,523.84 EUR of National and ESIF Funding. The project has been started on 1st October 2019

Control Design and Performance Analysis for Autonomous Formation Flight Experiments

Autonomous Formation Flight is a key approach for reducing greenhouse gas emissions and managing traffic in future high density airspace. Unmanned Aerial Vehicles (UAV’s) have made it possible for the physical demonstration and validation of autonomous formation flight concepts inexpensively and eliminates the flight risk to human pilots. This thesis discusses the design, implementation, and flight testing of three different formation flight control methods, Proportional Integral and Derivative (PID); Fuzzy Logic (FL); and NonLinear Dynamic Inversion (NLDI), and their respective performance behavior. Experimental results show achievable autonomous formation flight and performance quality with a pair of low-cost unmanned research fixed wing aircraft and also with a solo vertical takeoff and landing (VTOL) quadrotor

Communication and Control in Collaborative UAVs: Recent Advances and Future Trends

The recent progress in unmanned aerial vehicles (UAV) technology has significantly advanced UAV-based applications for military, civil, and commercial domains. Nevertheless, the challenges of establishing high-speed communication links, flexible control strategies, and developing efficient collaborative decision-making algorithms for a swarm of UAVs limit their autonomy, robustness, and reliability. Thus, a growing focus has been witnessed on collaborative communication to allow a swarm of UAVs to coordinate and communicate autonomously for the cooperative completion of tasks in a short time with improved efficiency and reliability. This work presents a comprehensive review of collaborative communication in a multi-UAV system. We thoroughly discuss the characteristics of intelligent UAVs and their communication and control requirements for autonomous collaboration and coordination. Moreover, we review various UAV collaboration tasks, summarize the applications of UAV swarm networks for dense urban environments and present the use case scenarios to highlight the current developments of UAV-based applications in various domains. Finally, we identify several exciting future research direction that needs attention for advancing the research in collaborative UAVs

Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey

The ongoing amalgamation of UAV and ML techniques is creating a significant synergy and empowering UAVs with unprecedented intelligence and autonomy. This survey aims to provide a timely and comprehensive overview of ML techniques used in UAV operations and communications and identify the potential growth areas and research gaps. We emphasise the four key components of UAV operations and communications to which ML can significantly contribute, namely, perception and feature extraction, feature interpretation and regeneration, trajectory and mission planning, and aerodynamic control and operation. We classify the latest popular ML tools based on their applications to the four components and conduct gap analyses. This survey also takes a step forward by pointing out significant challenges in the upcoming realm of ML-aided automated UAV operations and communications. It is revealed that different ML techniques dominate the applications to the four key modules of UAV operations and communications. While there is an increasing trend of cross-module designs, little effort has been devoted to an end-to-end ML framework, from perception and feature extraction to aerodynamic control and operation. It is also unveiled that the reliability and trust of ML in UAV operations and applications require significant attention before full automation of UAVs and potential cooperation between UAVs and humans come to fruition.Comment: 36 pages, 304 references, 19 Figure

Autonomous Unmanned Aerial Vehicle Navigation using Reinforcement Learning: A Systematic Review

There is an increasing demand for using Unmanned Aerial Vehicle (UAV), known as drones, in different applications such as packages delivery, traffic monitoring, search and rescue operations, and military combat engagements. In all of these applications, the UAV is used to navigate the environment autonomously --- without human interaction, perform specific tasks and avoid obstacles. Autonomous UAV navigation is commonly accomplished using Reinforcement Learning (RL), where agents act as experts in a domain to navigate the environment while avoiding obstacles. Understanding the navigation environment and algorithmic limitations plays an essential role in choosing the appropriate RL algorithm to solve the navigation problem effectively. Consequently, this study first identifies the main UAV navigation tasks and discusses navigation frameworks and simulation software. Next, RL algorithms are classified and discussed based on the environment, algorithm characteristics, abilities, and applications in different UAV navigation problems, which will help the practitioners and researchers select the appropriate RL algorithms for their UAV navigation use cases. Moreover, identified gaps and opportunities will drive UAV navigation research

The use of modern tools for modelling and simulation of UAV with Haptic

Unmanned Aerial Vehicle (UAV) is a research field in robotics which is in high demand in recent years, although there still exist many unanswered questions. In contrast, to the human operated aerial vehicles, it is still far less used to the fact that people are dubious about flying in or flying an unmanned vehicle. It is all about giving the control right to the computer (which is the Artificial Intelligence) for making decisions based on the situation like human do but this has not been easy to make people understand that it’s safe and to continue the enhancement on it. These days there are many types of UAVs available in the market for consumer use, for applications like photography to play games, to map routes, to monitor buildings, for security purposes and much more. Plus, these UAVs are also being widely used by the military for surveillance and for security reasons. One of the most commonly used consumer product is a quadcopter or quadrotor. The research carried out used modern tools (i.e., SolidWorks, Java Net Beans and MATLAB/Simulink) to model controls system for Quadcopter UAV with haptic control system to control the quadcopter in a virtual simulation environment and in real time environment. A mathematical model for the controlling the quadcopter in simulations and real time environments were introduced. Where, the design methodology for the quadcopter was defined. This methodology was then enhanced to develop a virtual simulation and real time environments for simulations and experiments. Furthermore, the haptic control was then implemented with designed control system to control the quadcopter in virtual simulation and real time experiments. By using the mathematical model of quadcopter, PID & PD control techniques were used to model the control setup for the quadcopter altitude and motion controls as work progressed. Firstly, the dynamic model is developed using a simple set of equations which evolves further by using complex control & mathematical model with precise function of actuators and aerodynamic coefficients Figure5-7. The presented results are satisfying and shows that flight experiments and simulations of the quadcopter control using haptics is a novel area of research which helps perform operations more successfully and give more control to the operator when operating in difficult environments. By using haptic accidents can be minimised and the functional performance of the operator and the UAV will be significantly enhanced. This concept and area of research of haptic control can be further developed accordingly to the needs of specific applications