Flight coordination solutions for multirotor unmanned aerial vehicles

[EN] As the popularity and the number of Unmanned Aerial Vehicles (UAVs) increases, new protocols are needed to coordinate them when flying without direct human control, and to avoid that these UAVs collide with each other. Testing such novel protocols on real UAVs is a complex procedure that requires investing much time, money and research efforts. Hence, it becomes necessary to first test the developed solutions using simulation. Unfortunately, existing tools present significant limitations: some of them only simulate accurately the flight behavior of a single UAV, while some other simulators can manage several UAVs simultaneously, but not in real time, thus losing accuracy regarding the mobility pattern of the UAV. In this work we address such problem by introducing Arducopter Simulator (ArduSim), a novel simulation platform that allows controlling in soft real-time the flight and communications of multiple UAVs, being the developed protocols directly portable to real devices. Moreover, ArduSim includes a realistic model for the WiFi communications link between UAVs, which was proposed based on real experiments. The chances that two UAVs get close to each other during their flights is increasing as more and more of them populate our skies, causing concerns regarding potential collisions. Therefore, this thesis also proposes the Mission Based Collision Avoidance Protocol (MBCAP), a novel UAV collision avoidance protocol applicable to all types of multicopters flying autonomously. It relies on wireless communications in order to detect nearby UAVs, and to negotiate the procedure to avoid any potential collision. Experimental and simulation results demonstrate the validity and effectiveness of the proposed solution, which typically introduces a small overhead in the range of 15 to 42 seconds for each risky situation successfully handled. The previous solution aims at UAVs performing independent flights, but they can also form a swarm, where more constraints have to be met to avoid collisions among them, and to allow them to complete their task efficiently. Deploying an UAV swarm instead of a single UAV can provide additional benefits when, for example, cargo carrying requirements exceed the lifting power of a single UAV, or when the deployment of several UAVs simultaneously can accelerate the accomplishment of the mission, and broaden the covered area. To this aim, in this work we present the Mission-based UAV Swarm Coordination Protocol (MUSCOP), a solution that allows multiple UAVs to perfectly coordinate their flight when performing planned missions. Experimental results show that the proposed protocol is able to achieve a high degree of swarm cohesion independently of the flight formation adopted, and even in the presence of very lossy channels, achieving minimal synchronization delays and very low position offsets with regard to the ideal case. Currently, there are some other scenarios, such as search and rescue operations, where the deployment of manually guided swarms of UAVs can be necessary. In such cases, the pilot's commands are unknown a priori (unpredictable), meaning that the UAVs must respond in near real-time to the movements of the leader UAV in order to maintain swarm consistency. Hence, in this thesis we also propose the FollowMe protocol for the coordination of UAVs in a swarm where the swarm leader is controlled by a real pilot, and the other UAVs must follow it in real-time to maintain swarm cohesion. Simulation results show the validity of the proposed swarm coordination protocol, detailing the responsiveness limits of our solution, and finding the minimum distances between UAVs to avoid collisions.[ES] A medida que la popularidad de los Vehículos Aéreos No Tripulados (VANTs) se incrementa, también se hacen necesarios nuevos protocolos para coordinarlos en vuelos sin control humano directo, y para evitar que colisionen entre sí. Probar estos nuevos protocolos en VANTs reales es un proceso complejo que requiere invertir mucho tiempo, dinero y esfuerzo investigador. Por lo tanto, es necesario probar en simulación las soluciones previamente implementadas. Lamentablemente, las herramientas actuales tienen importantes limitaciones: algunas simulan con precisión el vuelo de un único VANT, mientras que otros simuladores pueden gestionar varios VANTs simultáneamente aunque no en tiempo real, perdiendo por lo tanto precisión en el patrón de movilidad del VANT. En este trabajo abordamos este problema introduciendo Arducopter Simulator (ArduSim), una nueva plataforma de simulación que permite controlar en tiempo real el vuelo y la comunicación entre múltiples VANTs, permitiendo llevar los protocolos desarrollados a dispositivos reales con facilidad. Además, ArduSim incluye un modelo realista de un enlace de comunicaciones WiFi entre VANTs, el cual está basado en el resultado obtenido de experimentos con VANTs reales. La posibilidad de que dos VANTs se aproximen entre sí durante el vuelo se incrementa a medida que hay más aeronaves de este tipo surcando los cielos, introduciendo peligro por posibles colisiones. Por ello, esta tesis propone Mission Based Collision Avoidance Protocol (MBCAP), un nuevo protocolo de evitación de colisiones para VANTs aplicable a todo tipo de multicópteros mientras vuelan autónomamente. MBCAP utiliza comunicaciones inalámbricas para detectar VANTs cercanos y para negociar el proceso de evitación de la colisión. Los resultados de simulaciones y experimentos reales demuestran la validez y efectividad de la solución propuesta, que introduce un pequeño aumento del tiempo de vuelo de entre 15 y 42 segundos por cada situación de riesgo correctamente resuelta. La solución anterior está orientada a VANTs que realizan vuelos independientes, pero también pueden formar un enjambre, donde hay que cumplir más restricciones para evitar que colisionen entre sí, y para que completen la tarea de forma eficiente. Desplegar un enjambre de VANTs en vez de uno solo proporciona beneficios adicionales cuando, por ejemplo, la necesidad de carga excede la capacidad de elevación de un único VANT, o cuando al desplegar varios VANTs simultáneamente se acelera la misión y se cubre un área mayor. Con esta finalidad, en este trabajo presentamos el protocolo Mission-based UAV Swarm Coordination Protocol (MUSCOP), una solución que permite a varios VANTs coordinar perfectamente el vuelo mientras realizan misiones planificadas. Los resultados experimentales muestran que el protocolo propuesto permite al enjambre alcanzar un grado de cohesión elevado independientemente de la formación de vuelo adoptada, e incluso en presencia de un canal de comunicación con muchas pérdidas, consiguiendo retardos en la sincronización insignificantes y desfases mínimos en la posición con respecto al caso ideal. Actualmente hay otros escenarios, como las operaciones de búsqueda y rescate, donde el despliegue de enjambres de VANTs guiados manualmente puede ser necesario. En estos casos, las órdenes del piloto son desconocidas a priori (impredecibles), lo que significa que los VANTs deben responder prácticamente en tiempo real a los movimientos del VANT líder para mantener la consistencia del enjambre. Por ello, en esta tesis proponemos el protocolo FollowMe para la coordinación de VANTs en un enjambre donde el líder es controlado por un piloto, y el resto de VANTs lo siguen en tiempo real para mantener la cohesión del enjambre. Las simulaciones muestran la validez del protocolo de coordinación de enjambres propuesto, detallando los límites de la solución, y definiendo la distancia mínima entre VANTs para evita[CA] A mesura que la popularitat dels Vehicles Aeris No Tripulats (VANTs) s'incrementa, també es fan necessaris nous protocols per a coordinar-los en vols sense control humà directe, i per a evitar que col·lisionen entre si. Provar aquests nous protocols en VANTs reals és un procés complex que requereix invertir molt de temps, diners i esforç investigador. Per tant, és necessari provar en simulació les solucions prèviament implementades. Lamentablement, les eines actuals tenen importants limitacions: algunes simulen amb precisió el vol d'un únic VANT, mentre que altres simuladors poden gestionar diversos VANTs simultàniament encara que no en temps real, perdent per tant precisió en el patró de mobilitat del VANT. En aquest treball abordem aquest problema introduint Arducopter Simulator (ArduSim), una nova plataforma de simulació que permet controlar en temps real el vol i la comunicació entre múltiples VANTs, permetent portar els protocols desenvolupats a dispositius reals amb facilitat. A més, ArduSim inclou un model realista d'un enllaç de comunicacions WiFi entre VANTs, que està basat en el resultat obtingut d'experiments amb VANTs reals. La possibilitat que dues VANTs s'aproximen entre si durant el vol s'incrementa a mesura que hi ha més aeronaus d'aquest tipus solcant els cels, introduint perill per possibles col·lisions. Per això, aquesta tesi proposa Mission Based Collision Avoidance Protocol (MBCAP), un nou protocol d'evitació de col·lisions per a VANTs aplicable a tota mena de multicòpters mentre volen autònomament. MBCAP utilitza comunicacions sense fils per a detectar VANTs pròxims i per a negociar el procés d'evitació de la col·lisió. Els resultats de simulacions i experiments reals demostren la validesa i efectivitat de la solució proposada, que introdueix un xicotet augment del temps de vol de entre 15 i 42 segons per cada situació de risc correctament resolta. La solució anterior està orientada a VANTs que realitzen vols independents, però també poden formar un eixam, on cal complir més restriccions per a evitar que col·lisionen entre si, i perquè completen la tasca de forma eficient. Desplegar un eixam de VANTs en comptes d'un només proporciona beneficis addicionals quan, per exemple, la necessitat de càrrega excedeix la capacitat d'elevació d'un únic VANT, o quan en desplegar diversos VANTs simultàniament s'accelera la missió i es cobreix una àrea major. Amb aquesta finalitat, en aquest treball presentem el protocol Mission-based UAV Swarm Coordination Protocol (MUSCOP), una solució que permet a diversos VANTs coordinar perfectament el vol mentre realitzen missions planificades. Els resultats experimentals mostren que el protocol proposat permet a l'eixam aconseguir un grau de cohesió elevat independentment de la formació de vol adoptada, i fins i tot en presència d'un canal de comunicació amb moltes pèrdues, aconseguint retards en la sincronització insignificants i desfasaments mínims en la posició respecte al cas ideal. Actualment hi ha altres escenaris, com les operacions de cerca i rescat, on el desplegament d'eixams de VANTs guiats manualment pot ser necessari. En aquests casos, les ordres del pilot són desconegudes a priori (impredictibles), el que significa que els VANTs han de respondre pràcticament en temps real als moviments del VANT líder per a mantindre la consistència de l'eixam. Per això, en aquesta tesi proposem el protocol FollowMe per a la coordinació de VANTs en un eixam on el líder és controlat per un pilot, i la resta de VANTs ho segueixen en temps real per a mantindre la cohesió de l'eixam. Les simulacions mostren la validesa del protocol de coordinació d'eixams proposat, detallant els límits de la solució, i definint la distància mínima entre VANTs per a evitar col·lisions.Fabra Collado, FJ. (2020). Flight coordination solutions for multirotor unmanned aerial vehicles [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/147857TESI

Avoiding contingent incidents by quadrotors due to one or two propellers failure

With the increasing impact of drones in our daily lives, safety issues have become a primary concern. In this study, a novel supervisor-based active fault-tolerant (FT) control system is presented for a rotary-wing quadrotor to maintain its pose in 3D space upon losing one or two propellers. Our approach allows the quadrotor to make controlled movements about a primary axis attached to the body-fixed frame. A multi-loop cascaded control architecture is designed to ensure robustness, stability, reference tracking, and safe landing. The altitude control is performed using a proportional-integral-derivative (PID) controller, whereas linear-quadratic-integral (LQI) and model-predictive-control (MPC) have been investigated for reduced attitude control and their performance is compared based on absolute and mean-squared error. The simulation results affirm that the quadrotor remains in a stable region, successfully performs the reference tracking, and ensures a safe landing while counteracting the effects of propeller(s) failures

Mission-based mobility models for UAV networks

Las redes UAV han atraído la atención de los investigadores durante la última década. Las numerosas posibilidades que ofrecen los sistemas single-UAV aumentan considerablemente al usar múltiples UAV. Sin embargo, el gran potencial del sistema multi-UAV viene con un precio: la complejidad de controlar todos los aspectos necesarios para garantizar que los UAVs cumplen la misión que se les ha asignado. Ha habido numerosas investigaciones dedicadas a los sistemas multi-UAV en el campo de la robótica en las cuales se han utilizado grupos de UAVs para diferentes aplicaciones. Sin embargo, los aspectos relacionados con la red que forman estos sistemas han comenzado a reclamar un lugar entre la comunidad de investigación y han hecho que las redes de UAVs se consideren como un nuevo paradigma entre las redes multi-salto. La investigación de redes de UAVs, de manera similar a otras redes multi-salto, se divide principalmente en dos categorías: i) modelos de movilidad que capturan la movilidad de la red, y ii) algoritmos de enrutamiento. Ambas categorías han heredado muchos algoritmos que pertenecían a las redes MANET, que fueron el primer paradigma de redes multi-salto que atrajo la atención de los investigadores. Aunque hay esfuerzos de investigación en curso que proponen soluciones para ambas categorías, el número de modelos de movilidad y algoritmos de enrutamiento específicos para redes UAV es limitado. Además, en el caso de los modelos de movilidad, las soluciones existentes propuestas son simplistas y apenas representan la movilidad real de un equipo de UAVs, los cuales se utilizan principalmente en operaciones orientadas a misiones, en la que cada UAV tiene asignados movimientos específicos. Esta tesis propone dos modelos de movilidad basados en misiones para una red de UAVs que realiza dos operaciones diferentes. El escenario elegido en el que se desarrollan las misiones corresponde con una región en la que ha ocurrido, por ejemplo, un desastre natural. La elección de este tipo de escenario se debe a que en zonas de desastre, la infraestructura de comunicaciones comúnmente está dañada o totalmente destruida. En este tipo de situaciones, una red de UAVs ofrece la posibilidad de desplegar rápidamente una red de comunicaciones. El primer modelo de movilidad, llamado dPSO-U, ha sido diseñado para capturar la movilidad de una red UAV en una misión con dos objetivos principales: i) explorar el área del escenario para descubrir las ubicaciones de los nodos terrestres, y ii) hacer que los UAVs converjan de manera autónoma a los grupos en los que se organizan los nodos terrestres (también conocidos como clusters). El modelo de movilidad dPSO-U se basa en el conocido algoritmo particle swarm optimization (PSO), considerando los UAV como las partículas del algoritmo, y también utilizando el concepto de valores dinámicos para la inercia, el local best y el neighbour best de manera que el modelo de movilidad tenga ambas capacidades: la de exploración y la de convergencia. El segundo modelo, denominado modelo de movilidad Jaccard-based, captura la movilidad de una red UAV que tiene asignada la misión de proporcionar servicios de comunicación inalámbrica en un escenario de mediano tamaño. En este modelo de movilidad se ha utilizado una combinación del virtual forces algorithm (VFA), de la distancia Jaccard entre cada par de UAVs y metaheurísticas como hill climbing y simulated annealing, para cumplir los dos objetivos de la misión: i) maximizar el número de nodos terrestres (víctimas) que se encuentran bajo el área de cobertura inalámbrica de la red UAV, y ii) mantener la red UAV como una red conectada, es decir, evitando las desconexiones entre UAV. Se han realizado simulaciones exhaustivas con herramientas software específicamente desarrolladas para los modelos de movilidad propuestos. También se ha definido un conjunto de métricas para cada modelo de movilidad. Estas métricas se han utilizado para validar la capacidad de los modelos de movilidad propuestos de emular los movimientos de una red UAV en cada misión.UAV networks have attracted the attention of the research community in the last decade. The numerous capabilities of single-UAV systems increase considerably by using multiple UAVs. The great potential of a multi-UAV system comes with a price though: the complexity of controlling all the aspects required to guarantee that the UAV team accomplish the mission that it has been assigned. There have been numerous research works devoted to multi-UAV systems in the field of robotics using UAV teams for different applications. However, the networking aspects of multi-UAV systems started to claim a place among the research community and have made UAV networks to be considered as a new paradigm among the multihop ad hoc networks. UAV networks research, in a similar manner to other multihop ad hoc networks, is mainly divided into two categories: i) mobility models that capture the network mobility, and ii) routing algorithms. Both categories have inherited previous algorithms mechanisms that originally belong to MANETs, being these the first multihop networking paradigm attracting the attention of researchers. Although there are ongoing research efforts proposing solutions for the aforementioned categories, the number of UAV networks-specific mobility models and routing algorithms is limited. In addition, in the case of the mobility models, the existing solutions proposed are simplistic and barely represent the real mobility of a UAV team, which are mainly used in missions-oriented operations. This thesis proposes two mission-based mobility models for a UAV network carrying out two different operations over a disaster-like scenario. The reason for selecting a disaster scenario is because, usually, the common communication infrastructure is malfunctioning or completely destroyed. In these cases, a UAV network allows building a support communication network which is rapidly deployed. The first mobility model, called dPSO-U, has been designed for capturing the mobility of a UAV network in a mission with two main objectives: i) exploring the scenario area for discovering the location of ground nodes, and ii) making the UAVs to autonomously converge to the groups in which the nodes are organized (also referred to as clusters). The dPSO-U mobility model is based on the well-known particle swarm optimization algorithm (PSO), considering the UAVs as the particles of the algorithm, and also using the concept of dynamic inertia, local best and neighbour best weights so the mobility model can have both abilities: exploration and convergence. The second one, called Jaccard-based mobility model, captures the mobility of a UAV network that has been assigned with the mission of providing wireless communication services in a medium-scale scenario. A combination of the virtual forces algorithm (VFA), the Jaccard distance between each pair of UAVs and metaheuristics such as hill climbing or simulated annealing have been used in this mobility model in order to meet the two mission objectives: i) to maximize the number of ground nodes (i.e. victims) under the UAV network wireless coverage area, and ii) to maintain the UAV network as a connected network, i.e. avoiding UAV disconnections. Extensive simulations have been performed with software tools that have been specifically developed for the proposed mobility models. Also, a set of metrics have been defined and measured for each mobility model. These metrics have been used for validating the ability of the proposed mobility models to emulate the movements of a UAV network in each mission

Risk-sensitive motion planning for MAVs based on mission-related fault-tolerant analysis

Multirotor Aerial Vehicles may be fault-tolerant by design when rotor-failure is possible to measure or identify, especially when a large number of rotors are used. For instance, an octocopter can be capable to complete some missions even when a double-rotor fault occurs during the execution. In this paper, we study how a rotor-failure reduces the vehicle control admissible set and its importance with respect to the selected mission, i.e. we perform mission-related fault-tolerant analysis. Furthermore, we propose a risk-sensitive motion-planning algorithm capable to take into account the risks during the planning stage by means of mission-related fault-tolerant analysis. We show that the proposed approach is much less conservative in terms of selected performance measures than a conservative risk planner that assumes that the considered fault will certainly occur during the mission execution. As expected, the proposed risk-sensitive motion planner is also readier for accepting failures during the mission execution than the risk-insensitive approach that assumes no failure will occur

Flight evaluation of simultaneous actuator/sensor fault reconstruction on a quadrotor minidrone

This is the final version. Available on open access from Wiley via the DOI in this recordIn view of the increase in the number of Unmanned Aerial Vehicles (UAVs) in the commercial and private sectors, it is imperative to make sure that such systems are safe, and thus resilient to faults and failures. This paper considers the numerical design and practical implementation of a linear parametervarying (LPV) sliding mode observer for Fault Detection and Diagnosis (FDD) of a quadrotor minidrone. Starting from a nonlinear model of the minidrone, an LPV model is extracted for design, and the observer synthesis procedure, using Linear Matrix Inequalities (LMI), is detailed. Simulations of the observer FDD show good performance. The observer is then implemented on a Parrot® Rolling Spider minidrone and a series of flight tests is performed to assess the FDD capabilities in real time using its on-board processing power. The flight tests confirm the performance obtained in simulation, and show that the sliding mode observer is able to provide reliable fault reconstruction for quadrotor minidrone systems.Engineering and Physical Sciences Research Council (EPSRC

Design and control of a collision-resilient aerial vehicle with an icosahedron tensegrity structure

We present the tensegrity aerial vehicle, a design of collision-resilient rotor robots with icosahedron tensegrity structures. The tensegrity aerial vehicles can withstand high-speed impacts and resume operation after collisions. To guide the design process of these aerial vehicles, we propose a model-based methodology that predicts the stresses in the structure with a dynamics simulation and selects components that can withstand the predicted stresses. Meanwhile, an autonomous re-orientation controller is created to help the tensegrity aerial vehicles resume flight after collisions. The re-orientation controller can rotate the vehicles from arbitrary orientations on the ground to ones easy for takeoff. With collision resilience and re-orientation ability, the tensegrity aerial vehicles can operate in cluttered environments without complex collision-avoidance strategies. Moreover, by adopting an inertial navigation strategy of replacing flight with short hops to mitigate the growth of state estimation error, the tensegrity aerial vehicles can conduct short-range operations without external sensors. These capabilities are validated by a test of an experimental tensegrity aerial vehicle operating with only onboard inertial sensors in a previously-unknown forest.Comment: 12 pages, 16 figure

Flight controller synthesis via deep reinforcement learning

Traditional control methods are inadequate in many deployment settings involving autonomous control of Cyber-Physical Systems (CPS). In such settings, CPS controllers must operate and respond to unpredictable interactions, conditions, or failure modes. Dealing with such unpredictability requires the use of executive and cognitive control functions that allow for planning and reasoning. Motivated by the sport of drone racing, this dissertation addresses these concerns for state-of-the-art flight control by investigating the use of deep artificial neural networks to bring essential elements of higher-level cognition to bear on the design, implementation, deployment, and evaluation of low level (attitude) flight controllers. First, this thesis presents a feasibility analyses and results which confirm that neural networks, trained via reinforcement learning, are more accurate than traditional control methods used by commercial uncrewed aerial vehicles (UAVs) for attitude control. Second, armed with these results, this thesis reports on the development and release of an open source, full solution stack for building neuro-flight controllers. This stack consists of a tuning framework for implementing training environments (GymFC) and firmware for the world’s first neural network supported flight controller (Neuroflight). GymFC’s novel approach fuses together the digital twinning paradigm with flight control training to provide seamless transfer to hardware. Third, to transfer models synthesized by GymFC to hardware, this thesis reports on the toolchain that has been released for compiling neural networks into Neuroflight, which can be flashed to off-the-shelf microcontrollers. This toolchain includes detailed procedures for constructing a multicopter digital twin to allow the research and development community to synthesize flight controllers unique to their own aircraft. Finally, this thesis examines alternative reward system functions as well as changes to the software environment to bridge the gap between simulation and real world deployment environments. The design, evaluation, and experimental work summarized in this thesis demonstrates that deep reinforcement learning is able to be leveraged for the design and implementation of neural network controllers capable not only of maintaining stable flight, but also precision aerobatic maneuvers in real world settings. As such, this work provides a foundation for developing the next generation of flight control systems

A Model Predictive Path Integral Method for Fast, Proactive, and Uncertainty-Aware UAV Planning in Cluttered Environments

Current motion planning approaches for autonomous mobile robots often assume that the low level controller of the system is able to track the planned motion with very high accuracy. In practice, however, tracking error can be affected by many factors, and could lead to potential collisions when the robot must traverse a cluttered environment. To address this problem, this paper proposes a novel receding-horizon motion planning approach based on Model Predictive Path Integral (MPPI) control theory -- a flexible sampling-based control technique that requires minimal assumptions on vehicle dynamics and cost functions. This flexibility is leveraged to propose a motion planning framework that also considers a data-informed risk function. Using the MPPI algorithm as a motion planner also reduces the number of samples required by the algorithm, relaxing the hardware requirements for implementation. The proposed approach is validated through trajectory generation for a quadrotor unmanned aerial vehicle (UAV), where fast motion increases trajectory tracking error and can lead to collisions with nearby obstacles. Simulations and hardware experiments demonstrate that the MPPI motion planner proactively adapts to the obstacles that the UAV must negotiate, slowing down when near obstacles and moving quickly when away from obstacles, resulting in a complete reduction of collisions while still producing lively motion.Comment: Accepted to IROS 2023, 8 page