Adaptive and Optimal Motion Control of Multi-UAV Systems

This thesis studies trajectory tracking and coordination control problems for single and multi unmanned aerial vehicle (UAV) systems. These control problems are addressed for both quadrotor and fixed-wing UAV cases. Despite the fact that the literature has some approaches for both problems, most of the previous studies have implementation challenges on real-time systems. In this thesis, we use a hierarchical modular approach where the high-level coordination and formation control tasks are separated from low-level individual UAV motion control tasks. This separation helps efficient and systematic optimal control synthesis robust to effects of nonlinearities, uncertainties and external disturbances at both levels, independently. The modular two-level control structure is convenient in extending single-UAV motion control design to coordination control of multi-UAV systems. Therefore, we examine single quadrotor UAV trajectory tracking problems to develop advanced controllers compensating effects of nonlinearities and uncertainties, and improving robustness and optimality for tracking performance. At fi rst, a novel adaptive linear quadratic tracking (ALQT) scheme is developed for stabilization and optimal attitude control of the quadrotor UAV system. In the implementation, the proposed scheme is integrated with Kalman based reliable attitude estimators, which compensate measurement noises. Next, in order to guarantee prescribed transient and steady-state tracking performances, we have designed a novel backstepping based adaptive controller that is robust to effects of underactuated dynamics, nonlinearities and model uncertainties, e.g., inertial and rotational drag uncertainties. The tracking performance is guaranteed to utilize a prescribed performance bound (PPB) based error transformation. In the coordination control of multi-UAV systems, following the two-level control structure, at high-level, we design a distributed hierarchical (leader-follower) 3D formation control scheme. Then, the low-level control design is based on the optimal and adaptive control designs performed for each quadrotor UAV separately. As particular approaches, we design an adaptive mixing controller (AMC) to improve robustness to varying parametric uncertainties and an adaptive linear quadratic controller (ALQC). Lastly, for planar motion, especially for constant altitude flight of fixed-wing UAVs, in 2D, a distributed hierarchical (leader-follower) formation control scheme at the high-level and a linear quadratic tracking (LQT) scheme at the low-level are developed for tracking and formation control problems of the fixed-wing UAV systems to examine the non-holonomic motion case. The proposed control methods are tested via simulations and experiments on a multi-quadrotor UAV system testbed

Safe and accurate MAV Control, navigation and manipulation

This work focuses on the problem of precise, aggressive and safe Micro Aerial Vehicle (MAV) navigation as well as deployment in applications which require physical interaction with the environment. To address these issues, we propose three different MAV model based control algorithms that rely on the concept of receding horizon control. As a starting point, we present a computationally cheap algorithm which utilizes an approximate linear model of the system around hover and is thus maximally accurate for slow reference maneuvers. Aiming at overcoming the limitations of the linear model parameterisation, we present an extension to the first controller which relies on the true nonlinear dynamics of the system. This approach, even though computationally more intense, ensures that the control model is always valid and allows tracking of full state aggressive trajectories. The last controller addresses the topic of aerial manipulation in which the versatility of aerial vehicles is combined with the manipulation capabilities of robotic arms. The proposed method relies on the formulation of a hybrid nonlinear MAV-arm model which also takes into account the effects of contact with the environment. Finally, in order to enable safe operation despite the potential loss of an actuator, we propose a supervisory algorithm which estimates the health status of each motor. We further showcase how this can be used in conjunction with the nonlinear controllers described above for fault tolerant MAV flight. While all the developed algorithms are formulated and tested using our specific MAV platforms (consisting of underactuated hexacopters for the free flight experiments, hexacopter-delta arm system for the manipulation experiments), we further discuss how these can be applied to other underactuated/overactuated MAVs and robotic arm platforms. The same applies to the fault tolerant control where we discuss different stabilisation techniques depending on the capabilities of the available hardware. Even though the primary focus of this work is on feedback control, we thoroughly describe the custom hardware platforms used for the experimental evaluation, the state estimation algorithms which provide the basis for control as well as the parameter identification required for the formulation of the various control models. We showcase all the developed algorithms in experimental scenarios designed to highlight the corresponding strengths and weaknesses as well as show that the proposed methods can run in realtime on commercially available hardware.Open Acces

Autonomous Navigation of Quadrotor Swarms

RÉSUMÉ La mise sur le marché de composants toujours plus performants et compétitifs en termes de coût, ainsi que le développement rapide des technologies de commande et de navigation en robotique, nous ont amenés à envisager le contrôle d’un large essaim de quadrirotors. Di-verses solutions impliquant des drones existent déjà pour différentes applications: inventaire forestier, gestion du littoral, suivi du trafic, etc. Parmi celles-ci, la recherche et le sauvetage en situation d’urgence représentent à nos yeux la possibilité la plus intéressante et constitue, de fait, la première motivation de notre travail. Par conséquent, une large revue de littérature sur la question est fournie. Ce travail se concentre sur le contrôle de l’essaim lui-même, et non sur l’application finale. Tout d’abord, un modèle mathématique de la dynamique du quadrirotor est présenté et plusieurs lois de commande numérique sont synthétisées. Ces dernières implémentent les modes de fonctionnement nécessaires aux algorithmes de navigation, à savoir : commande en vitesse, commande en position et commande en suivi. Ensuite, deux solutions originales et complémentaires de contrôle d’essaim sont proposées. D’une part, un algorithme d’essaimage pour la navigation extérieure est développé. Contrairement à la plupart des travaux trouvés dans la littérature, la solution proposée ici gère non seulement le maintien, mais aussi l’initialisation de la formation. Plus spécifiquement, un modèle de formation hexagonale est introduit. Ensuite, les places en formation sont attribuées de façon optimale à l’aide de l’algorithme hongrois. Enfin, les agents se déplacent jusqu’à la place qui leur est assignée tout en évitant les autres agents avec un algorithme de navigation inspiré du Artificial Potential Field. De plus, cette solution tient compte de contraintes de conception réalistes et a été intégrée avec succès dans un logiciel embarqué de quadrotor déjà existant et opérationnel. Les résultats de simulations Software-In-The-Loop sont fournis. D’autre part, une solution d’essaimage pour la navigation intérieure est étudiée. L’algorithme proposé implémente un certain nombre de comportements individuels simples, de sorte qu’un grand essaim peut suivre un meneur dans des environnements encombrés en se fiant uniquement aux informations locales. Des simulations préliminaires sont effectuées et les résultats montrent qu’il serait possible de faire fonctionner, conformément au besoin étudié, un essaim de cent quadrirotors avec l’algorithme proposé. En particulier, l’essaim est capable de suivre le meneur, de maintenir la connectivité, d’éviter les collisions entre agents, d’éviter les obstacles et même de se faufiler dans des espaces étroits.----------ABSTRACT The ever-growing hardware capabilities and the rapid development of robotic control and navigation technologies have led us to consider the control of an entire swarm of quadrotors. Drone-based solutions have been developed for different applications: forest inventory, coastal management, traÿc monitoring, etc... Among these, the Search And Rescue application represents for us a very promising field of application and constitutes the first motivation of our work. As a result, a wide literature review on the matter is provided. Nevertheless, this work focuses on the swarm control itself, and not on the end user application. First, a mathematical model of the quadrotor dynamics is presented and several digital control laws are designed. The latter provide operating modes useful for the navigation algorithms, namely: velocity control, position control and tracking control. Then, two original and complimentary swarming solutions are proposed. On the one hand, a swarming algorithm for outdoor navigation is developed. Unlike most of the works reviewed in the literature, our solution handles not only the maintenance but also the initialization of the formation. More specifically, an hexagonal formation pattern is intro-duced. Then, positions are optimally assigned using the Hungarian algorithm. Finally, the agents move to their assigned position while avoiding collisions with the other fleet members thanks to a navigation algorithm inspired from Artificial Potential Field. In addition, this solution accounts for realistic design constraints and was successfully integrated into already existing quadrotor onboard software. Software-In-The-Loop simulation results are provided. On the other hand, a swarming solution for indoor navigation is investigated. The proposed algorithm enforces a certain set of expected individual simple behaviors such that a large swarm can follow a leader through cluttered environments relying only on local information. Preliminary simulations are run and the results show that it is possible to operate a swarm of a hundred quadrotors with the proposed algorithm. In particular, the swarm is able to follow the leader, maintain connectivity, avoid collisions with the other agents, avoid obstacles, and even squeeze to pass through narrow spaces

Design and Modeling of Smartphone Controlled Vehicle

While many have worked on the transition phases of more popular hybrid aerial vehicle configurations, In this paper, we explore a novel multi-mode hybrid Unmanned Aerial Vehicle (UAV). Due to its expanded flying range and adaptability, hybrid aerial vehicles—which integrates two or more operating configurations—have become more and more widespread. The stages of transition between these modes are reasonably important whether there are two or more flight forms present. Whereas numerous have worked on the early stages of more widely used hybrid aerial vehicle types, in this paper a brand-new multi-mode hybrid UAV will be investigated. In order to fully exploit the vehicle's propulsion equipment and aerodynamic surfaces in both a horizontal cruising configuration and a vertical hovering configuration, we combine a tailless fixed-wing with a four-wing monocopter. By increasing construction integrity over the whole operational range, this lowers drag and wasteful mass when the aircraft is in motion in both modes. The transformation between the two flight states can be carried out in midair with just its current flying actuators and sensors. Through a ground controller, this vehicle may be operated by an Android device

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

A systematic review of perception system and simulators for autonomous vehicles research

This paper presents a systematic review of the perception systems and simulators for autonomous vehicles (AV). This work has been divided into three parts. In the first part, perception systems are categorized as environment perception systems and positioning estimation systems. The paper presents the physical fundamentals, principle functioning, and electromagnetic spectrum used to operate the most common sensors used in perception systems (ultrasonic, RADAR, LiDAR, cameras, IMU, GNSS, RTK, etc.). Furthermore, their strengths and weaknesses are shown, and the quantification of their features using spider charts will allow proper selection of different sensors depending on 11 features. In the second part, the main elements to be taken into account in the simulation of a perception system of an AV are presented. For this purpose, the paper describes simulators for model-based development, the main game engines that can be used for simulation, simulators from the robotics field, and lastly simulators used specifically for AV. Finally, the current state of regulations that are being applied in different countries around the world on issues concerning the implementation of autonomous vehicles is presented.This work was partially supported by DGT (ref. SPIP2017-02286) and GenoVision (ref. BFU2017-88300-C2-2-R) Spanish Government projects, and the “Research Programme for Groups of Scientific Excellence in the Region of Murcia" of the Seneca Foundation (Agency for Science and Technology in the Region of Murcia – 19895/GERM/15)

Homography-Based State Estimation for Autonomous Exploration in Unknown Environments

This thesis presents the development of vision-based state estimation algorithms to enable a quadcopter UAV to navigate and explore a previously unknown GPS denied environment. These state estimation algorithms are based on tracked Speeded-Up Robust Features (SURF) points and the homography relationship that relates the camera motion to the locations of tracked planar feature points in the image plane. An extended Kalman filter implementation is developed to perform sensor fusion using measurements from an onboard inertial measurement unit (accelerometers and rate gyros) with vision-based measurements derived from the homography relationship. Therefore, the measurement update in the filter requires the processing of images from a monocular camera to detect and track planar feature points followed by the computation of homography parameters. The state estimation algorithms are designed to be independent of GPS since GPS can be unreliable or unavailable in many operational environments of interest such as urban environments. The state estimation algorithms are implemented using simulated data from a quadcopter UAV and then tested using post processed video and IMU data from flights of an autonomous quadcopter. The homography-based state estimation algorithm was effective, but accumulates drift errors over time due to the relativistic homography measurement of position

Communication-based UAV Swarm Missions

Unmanned aerial vehicles have developed rapidly in recent years due to technological advances. UAV technology can be applied to a wide range of applications in surveillance, rescue, agriculture and transport. The problems that can exist in these areas can be mitigated by combining clusters of drones with several technologies. For example, when a swarm of drones is under attack, it may not be able to obtain the position feedback provided by the Global Positioning System (GPS). This poses a new challenge for the UAV swarm to fulfill a specific mission. This thesis intends to use as few sensors as possible on the UAVs and to design the smallest possible information transfer between the UAVs to maintain the shape of the UAV formation in flight and to follow a predetermined trajectory. This thesis presents Extended Kalman Filter methods to navigate autonomously in a GPS-denied environment. The UAV formation control and distributed communication methods are also discussed and given in detail

Safe navigation and motion coordination control strategies for unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) have become very popular for many military and civilian applications including in agriculture, construction, mining, environmental monitoring, etc. A desirable feature for UAVs is the ability to navigate and perform tasks autonomously with least human interaction. This is a very challenging problem due to several factors such as the high complexity of UAV applications, operation in harsh environments, limited payload and onboard computing power and highly nonlinear dynamics. Therefore, more research is still needed towards developing advanced reliable control strategies for UAVs to enable safe navigation in unknown and dynamic environments. This problem is even more challenging for multi-UAV systems where it is more efficient to utilize information shared among the networked vehicles. Therefore, the work presented in this thesis contributes towards the state-of-the-art in UAV control for safe autonomous navigation and motion coordination of multi-UAV systems. The first part of this thesis deals with single-UAV systems. Initially, a hybrid navigation framework is developed for autonomous mobile robots using a general 2D nonholonomic unicycle model that can be applied to different types of UAVs, ground vehicles and underwater vehicles considering only lateral motion. Then, the more complex problem of three-dimensional (3D) collision-free navigation in unknown/dynamic environments is addressed. To that end, advanced 3D reactive control strategies are developed adopting the sense-and-avoid paradigm to produce quick reactions around obstacles. A special case of navigation in 3D unknown confined environments (i.e. tunnel-like) is also addressed. General 3D kinematic models are considered in the design which makes these methods applicable to different UAV types in addition to underwater vehicles. Moreover, different implementation methods for these strategies with quadrotor-type UAVs are also investigated considering UAV dynamics in the control design. Practical experiments and simulations were carried out to analyze the performance of the developed methods. The second part of this thesis addresses safe navigation for multi-UAV systems. Distributed motion coordination methods of multi-UAV systems for flocking and 3D area coverage are developed. These methods offer good computational cost for large-scale systems. Simulations were performed to verify the performance of these methods considering systems with different sizes

Low computational SLAM for an autonomous indoor aerial inspection vehicle

The past decade has seen an increase in the capability of small scale Unmanned Aerial Vehicle (UAV) systems, made possible through technological advancements in battery, computing and sensor miniaturisation technology. This has opened a new and rapidly growing branch of robotic research and has sparked the imagination of industry leading to new UAV based services, from the inspection of power-lines to remote police surveillance. Miniaturisation of UAVs have also made them small enough to be practically flown indoors. For example, the inspection of elevated areas in hazardous or damaged structures where the use of conventional ground-based robots are unsuitable. Sellafield Ltd, a nuclear reprocessing facility in the U.K. has many buildings that require frequent safety inspections. UAV inspections eliminate the current risk to personnel of radiation exposure and other hazards in tall structures where scaffolding or hoists are required. This project focused on the development of a UAV for the novel application of semi-autonomously navigating and inspecting these structures without the need for personnel to enter the building. Development exposed a significant gap in knowledge concerning indoor localisation, specifically Simultaneous Localisation and Mapping (SLAM) for use on-board UAVs. To lower the on-board processing requirements of SLAM, other UAV research groups have employed techniques such as off-board processing, reduced dimensionality or prior knowledge of the structure, techniques not suitable to this application given the unknown nature of the structures and the risk of radio-shadows. In this thesis a novel localisation algorithm, which enables real-time and threedimensional SLAM running solely on-board a computationally constrained UAV in heavily cluttered and unknown environments is proposed. The algorithm, based on the Iterative Closest Point (ICP) method utilising approximate nearest neighbour searches and point-cloud decimation to reduce the processing requirements has successfully been tested in environments similar to that specified by Sellafield Ltd