La motivation qui a initié le projet de recherche ANR « Démonstrateur Gun Launched Micro Air Vehicle » est le besoin d’avoir un engin portatif qui permettrait d’obtenir rapidement des images d’une zone d’intérêt située à quelques centaines de mètres, avec la possibilité de pouvoir observer l’intérieur des bâtiments à travers leurs fenêtres ou en allant les explorer directement. Pour répondre à ce besoin, l’Institut franco-allemand de recherche de St Louis a eu l’idée de lancer un minidrone hélicoptère avec un canon. Le GLMAV, sous la forme d’un projectile, est lancé à partir d‘un tube portable à une distance de 500 m et une altitude de 100 m, où il pourra commencer à transmettre des images de la zone à observer. L’utilisation d’un système hybride projectile/minidrone a deux principaux avantages : cela permet d’augmenter l’autonomie du drone, et les premières images de la zone d’intérêt sont obtenues très rapidement. Au cours de cette thèse, nous nous sommes intéressés à la phase de transition, passer d’un projectile à un mini hélicoptère. Un modèle aérodynamique détaillé du GLMAV a été obtenu sur toute son enveloppe de vol. En prenant en compte les difficultés rencontrées lors de la phase de transition (perturbations des capteurs dues à l’accélération de 2500g au lancement, conditions initiales variables), nous avons développé une stratégie de commande, et une loi de commande en vitesse basée sur la technique du backstepping. Cette stratégie de commande a été validée en simulation. La loi de commande en orientation a été validée sur le prototype du GLMAV. Des travaux sur le flux optique, pour obtenir les vitesses latérales, ont été commencés. / The motivation that initiated the ANR research project "Démonstrateur Gun Launched Micro Air Vehicle" is the need to have a portable system which would permi tto quickly obtain images of an zone of interest placed at some hundred of meters, with the possibility to observe inside buildings either by their windows or by going inside them.To answer this need, the French-German Research Institute of St Louis got the idea o fusing a gun launched rotorcraft-MAV. The GLMAV, in its projectile form, is launched from a portable launching tube to a distance of 500m and a height of 100m, where it will collect and transmit visual information from the scene. The use of a projectile/rotorcraft-MAV hybrid system has two main advantages : it allows extending the MAV range,and the first images of the interest zone are obtained very quickly. During this PhD, we studied the transition phase, the passage from a projectile to a rotorcraft-MAV. A detailed aerodynamic model of the GLMAV has been obtained over his whole flight envelope. Taking into account the difficulties encountered during the transition phase (perturbation of the sensors caused by the 2500g acceleration at the launch, varying initial conditions),we developed a control strategy, and a velocity control law based on the backstepping methodology. This control strategy has been validated in simulation. The attitude control law has been validated on the GLMAV prototype. Studies on optical flow, to obtain the lateral velocities of the GLMAV, have been started.

Furthering our understanding of the physics of flapping flight has the potential to benefit the field of micro air vehicles. Advancements in micro air vehicles can benefit applications such as surveillance, reconnaissance, and search and rescue. In this research, flapping kinematics of a ladybug was explored using a direct linear transformation. A flapping mechanism design is presented that was capable of executing ladybug or other species-specific kinematics. The mechanism was based on a differential gear design, had two wings, and could flap in harsh environments. This mechanism served as a test bed for force analysis and optimization studies. The first study was based on a Box-Behnken screening design to explore wing kinematic parameter design space and manually search in the direction of flapping kinematics that optimized the objective of maximum combined lift and thrust. The second study used a Box-Behnken screening design to build a response surface. Using gradient-based techniques, this surface was optimized for maximum combined lift and thrust. Box-Behnken design coupled with response surface methodology was an efficient method for exploring the mechanism force response. Both methods for optimization were capable of successfully improving lift and thrust force outputs. The incorporation of the results of these studies will aid in the design of more efficient micro air vehicles and with the ultimate goal of leading to a better understanding of flapping wing aerodynamics and the development of aerodynamic models.

No description available.

Ducted fan vehicles possess a superior ability to maximize payload capacity while minimizing vehicle size. Their ability to both hover and fly at high speed is a key advantage for information-gathering missions, particularly when close proximity to a target is essential. However, the ducted fan's aerodynamic characteristics pose difficulties for stable vehicle flight and therefore require complex control algorithms. In particular, they exhibit a large nose-up pitching moment during wind gusts and when transitioning from hover to forward flight. Understanding ducted fan aerodynamic behavior and how it can be altered through flow control techniques are the two prime objectives of this work. This dissertation provides a new paradigm for modeling the ducted fan's nonlinear behavior and new methods for changing the duct aerodynamics using active flow control. Steady and piezoelectric synthetic jet blowing are employed in the flow control concepts and are compared. The new aerodynamic model captures the nonlinear characteristics of the force, moment, and power data for a ducted fan, while representing these terms in a set of simple equations. The model attains excellent agreement with current and legacy experimental data using twelve non-dimensional constants. Synthetic jet actuators (SJA) have potential for use in flow control applications in UAVs with limited size, weight, and power budgets. Piezoelectric SJAs for a ducted fan vehicle were developed through two rounds of experimental designs. The final SJA design attained peak jet velocities in the range of 225 ft/sec (69 m/s) for a 0.03â x 0.80â rectangular slot. To reduce the magnitude of the nose-up pitching moment in cross-winds, two flow control concepts were explored: flow separation control at the duct lip, and flow turning at the duct trailing edge using a CoandÄ surface. Both concepts were experimentally proven to be successful. Synthetic jets and steady jets were capable of modifying the ducted fan flow to reduce pitching moment, but some cases required high values of steady blowing to create significant responses. Triggering leading edge separation on the duct lip was one application where synthetic jets showed comparable performance to steady jets operating at a blowing coefficient an order of magnitude higher. / Ph. D.

Les micro-drones sont des aéronefs sans pilotes de dimensions inférieures à un mètre et de poids inférieur à deux kilogrammes. Ils se distinguent des aéronefs classiques pour plusieurs raisons : un cycle de développement plus court, un coût plus faible, leur facilité d'opération et des configurations de véhicules spécifiques. L'ensemble de ces points attendent une réponse spécifique dans le développement des lois de commandes. Cette thèse s'y intéresse à travers deux problématiques : la commande d'une famille de drones quadrirotors et celle d'un drone hybride. Une famille de drones représente un même concept de véhicule décliné en plusieurs tailles dont on peut faire varier la charge utile ou son emplacement. Les lois de commandes doivent assurer un même niveau de performances malgré ses modifications. Un drone hybride se caractérise par sa capacité à réaliser du vol stationnaire et du vol d'avancement. Ces deux modes de vol ont chacun une dynamique de vol spécifique à laquelle les lois de commandes doivent s'adapter. Cette thèse présente la modélisation de quadrirotors et d'un drone hybride puis détaille une approche de commande adaptative indirecte qui répond aux problèmes introduits. La commande adaptative permet de garantir à l'aide d'un correcteur unique les performances de commande pour de multiples systèmes. Les méthodes d'estimation de paramètres et de synthèse linéaire à paramètres variants du schéma de commande sont décrites, puis, finalement, des résultats d'essais en vol montrent l'apport et les limites de cette approche. / Micro Air Vehicle are pilotless aircrafts with dimensions not exceeding one meter and a maximum weight of two kilograms. They are different from classical aircrafts for multiple reasons: a shorter development cycle, a cheaper development, their ease of operation and specific vehicle configurations. All these points expect a specific answer in the development of the control laws of the vehicles. This thesis considers this topic through two particular issues: the control of a family of quadrotors and the control of hybrid micro air vehicle. A family of quadrotor represents a single concept of vehicle but with various sizes, payloads and payload configurations. Control laws must guarantee the same level of performance despite all these modifications. A hybrid micro air vehicle is able to both hover like a helicopter and fly forward like a plane. These two flight modes have specific flight dynamics that the control laws must adapt to. This thesis first presents a model of quadrotors and hybrid micro air vehicle and then details an indirect adaptive control method to tackle both issues. Adaptive control should guarantee performance of multiple controlled systems with a single controller. The parameter estimation and linear parameter varying synthesis method of the adaptive control scheme are described and finally flight test results show the contributions and limits of the approach.

No description available.

Most micro air vehicles rely heavily on reliable GPS measurements for proper estimation and control, and therefore struggle in GPS-degraded environments. When GPS is not available, the global position and heading of the vehicle is unobservable. This dissertation establishes the theoretical and practical advantages of a relative navigation framework for MAV navigation in GPS-degraded environments. This dissertation explores how the consistency, accuracy, and stability of current navigation approaches degrade during prolonged GPS dropout and in the presence of heading uncertainty. Relative navigation (RN) is presented as an alternative approach that maintains observability by working with respect to a local coordinate frame. RN is compared with several current estimation approaches in a simulation environment and in hardware experiments. While still subject to global drift, RN is shown to produce consistent state estimates and stable control. Estimating relative states requires unique modifications to current estimation approaches. This dissertation further provides a tutorial exposition of the relative multiplicative extended Kalman filter, presenting how to properly ensure observable state estimation while maintaining consistency. The filter is derived using both inertial and body-fixed state definitions and dynamics. Finally, this dissertation presents a series of prolonged flight tests, demonstrating the effectiveness of the relative navigation approach for autonomous GPS-degraded MAV navigation in varied, unknown environments. The system is shown to utilize a variety of vision sensors, work indoors and outdoors, run in real-time with onboard processing, and not require special tuning for particular sensors or environments. Despite leveraging off-the-shelf sensors and algorithms, the flight tests demonstrate stable front-end performance with low drift. The flight tests also demonstrate the onboard generation of a globally consistent, metric, and localized map by identifying and incorporating loop-closure constraints and intermittent GPS measurements. With this map, mission objectives are shown to be autonomously completed.

This thesis focuses on the design and development of a fixed wing micro air vehicle (MAV) and on the development of randomized sampling based motion planning and control algorithms for path planning and stabilization of the MAV. In addition, the thesis also contains probabilis-tic analyses of the algorithmic properties of randomized sampling based algorithms, such as completeness and asymptotic optimality. The thesis begins with a detailed discussion on aerodynamic design, computational fluid dy-namic simulations of propeller wake, wind tunnel tests of a 150mm fixed wing micro air ve-hicle. The vehicle is designed in such a way that in spite of the various adverse effects of low Reynolds number aerodynamics and the complex propeller wake interactions with the airframe, the vehicle shows a balance of external forces and moments at most of the operating conditions. This is supported by various CFD analysis and wind tunnel tests and is shown in this thesis. The thesis also contains a reasonably accurate longitudinal and lateral dynamical model of the MAV, which are verified by numerous flight trials. However, there still exists a considerable amount of model uncertainties in the system descrip-tion of the MAV. A robust feedback stabilized close loop flight control law, is designed to attenuate the effects of modelling uncertainties, discrete vertical and head-on wind gusts, and to maintain flight stability and performance requirements at all allowable operating conditions. The controller is implemented in the MAV autopilot hardware with successful close loop flight trials. The flight controller is designed based on the probabilistic robust control approach. The approach is based on statistical average case analysis and synthesis techniques. It removes the conservatism present in the classical robust feedback design (which is based the worst case de-sign techniques) and associated sluggish system response characteristics. Instead of minimizing the effect of the worst case disturbance, a randomized techniques synthesizes a controller for which some performance index is minimized in an empirical average sense. In this thesis it is shown that the degree of conservatism in the design and the number of samples used to by the randomized sampling based techniques has a direct relationship. In particular, it is shown that, as the lower bound on the number of samples reduces, the degree of conservatism increases in the design. Classical motion planning and obstacle avoidance methodologies are computationally expen-sive with the number of degrees of freedom of the vehicle, and therefore, these methodologies are largely inapplicable for MAVs with 6 degrees of freedom. The problem of computational complexity can be avoided using randomized sampling based motion planning algorithms such as probabilistic roadmap method or PRM. However, as a pay-off these algorithms lack algorith-mic completeness properties. In this thesis, it is established that the algorithmic completeness properties are dependent on the choice of the sampling sequences. The thesis contains analy-sis of algorithmic features such as probabilistic completeness and asymptotic optimality of the PRM algorithm and its many variants, under the incremental and independent problem model framework. It is shown in this thesis that the structure of the random sample sequence affects the solution of the sampling based algorithms. The problem of capturing the connectivity of the configuration space in the presence of ob-stacles, which is a central problem in randomized motion planning, is also discussed in this thesis. In particular, the success probability of one such randomized algorithm, named Obsta-cle based Probabilistic Roadmap Method or OBPRM is estimated using geometric probability theory. A direct relationship between the weak upper bound of the success probability and the obstacle geometric features is established. The thesis also contains a new sampling based algorithm which is based on geometric random walk theory, which addresses the problem of capturing the connectivity of the configuration space. The algorithm shows better performance when compared with other similar algorithm such as the Randomized Bridge Builder method for identical benchmark problems. Numerical simulation shows that the algorithm shows en-hanced performance as the dimension of the motion planning problem increases. As one of the central objectives, the thesis proposes a pre-processing technique of the state space of the system to enhance the performance of sampling based kino-dynamic motion plan-ner such as rapidly exploring random tree or RRT. This pre-processing technique can not only be applied for the motion planning of the MAV, but can also be applied for a wide class of vehicle and complex systems with large number of degrees of freedom. The pre-processing techniques identifies the sequence of regions, to be searched for a solution, in order to do mo-tion planning and obstacle avoidance for an MAV, by an RRT planner. Numerical simulation shows significant improvement over the basic RRT planner with a small additional computa-tional overhead. The probabilistic analysis of RRT algorithm and an approximate asymptotic optimality analysis of the solution returned by the algorithm, is also presented in this thesis. In particular, it is shown that the RRT algorithm is not asymptotically optimal. An integral part of the motion planning algorithm is the capability of fast collision detection between various geometric objects. Image space based methods, which uses Graphics Pro-cessing Unit or GPU hardware, and do not use object geometry explicitly, are found to be fast and accurate for this purpose. In this thesis, a new collision detection method between two convex/non-convex objects using GPU, is provided. The performance of the algorithm, which is an extension of an existing algorithm, is verified with numerous collision detection scenarios.

Micro air vehicles have recently gained popularity due to their potential as autonomous systems. Their future impact, however, will depend in part on how well they can navigate in GPS-denied and GPS-degraded environments. In response to this need, this dissertation investigates a potential solution for GPS-denied operations called relative navigation. The method utilizes keyframe-to-keyframe odometry estimates and their covariances in a global back end that represents the global state as a pose graph. The back end is able to effectively represent nonlinear uncertainties and incorporate opportunistic global constraints. The GPS-denied research community has, for the most part, neglected to consider fixed-wing aircraft. This dissertation enables fixed-wing aircraft to utilize relative navigation by accounting for their sensing requirements. The development of an odometry-like, front-end, EKF-based estimator that utilizes only a monocular camera and an inertial measurement unit is presented. The filter uses the measurement model of the multi-state-constraint Kalman filter and regularly performs relative resets in coordination with keyframe declarations. In addition to the front-end development, a method is provided to account for front-end velocity bias in the back-end optimization. Finally a method is presented for enabling multiple vehicles to improve navigational accuracy by cooperatively sharing information. Modifications to the relative navigation architecture are presented that enable decentralized, cooperative operations amidst temporary communication dropouts. The proposed framework also includes the ability to incorporate inter-vehicle measurements and utilizes a new concept called the coordinated reset, which is necessary for optimizing the cooperative odometry and improving localization. Each contribution is demonstrated through simulation and/or hardware flight testing. Simulation and Monte-Carlo testing is used to show the expected quality of the results. Hardware flight-test results show the front-end estimator performance, several back-end optimization examples, and cooperative GPS-denied operations.

Micro aerial vehicles and other autonomous systems have the potential to truly transform life as we know it, however much of the potential of autonomous systems remains unrealized because reliable navigation is still an unsolved problem with significant challenges. This dissertation presents solutions to many aspects of autonomous navigation. First, it presents ROSflight, a software and hardware architure that allows for rapid prototyping and experimentation of autonomy algorithms on MAVs with lightweight, efficient flight control. Next, this dissertation presents improvments to the state-of-the-art in optimal control of quadrotors by utilizing the error-state formulation frequently utilized in state estimation. It is shown that performing optimal control directly over the error-state results in a vastly more computationally efficient system than competing methods while also dealing with the non-vector rotation components of the state in a principled way. In addition, real-time robust flight planning is considered with a method to navigate cluttered, potentially unknown scenarios with real-time obstacle avoidance. Robust state estimation is a critical component to reliable operation, and this dissertation focuses on improving the robustness of visual-inertial state estimation in a filtering framework by extending the state-of-the-art to include better modeling and sensor fusion. Further, this dissertation takes concepts from the visual-inertial estimation community and applies it to tightly-coupled GNSS, visual-inertial state estimation. This method is shown to demonstrate significantly more reliable state estimation than visual-inertial or GNSS-inertial state estimation alone in a hardware experiment through a GNSS-GNSS denied transition flying under a building and back out into open sky. Finally, this dissertation explores a novel method to combine measurements from multiple agents into a coherent map. Traditional approaches to this problem attempt to solve for the position of multiple agents at specific times in their trajectories. This dissertation instead attempts to solve this problem in a relative context, resulting in a much more robust approach that is able to handle much greater intial error than traditional approaches.