Les drones ou systèmes de drones aériens jouent un rôle de plus en plus important danstous les domaines, spécialement les drones à décollage et atterrissage verticaux. L’un desplus connus est le Quadrotor et, sans doute, il est la plateforme de recherche la plus utilisée.Cette thèse traite le problème de l’estimation et de la commande d’attitude appliqué àun corps rigide se déplaçant dans l’espace 3D tel que le Quadrotor. La première contributionde cette thèse est la conception et l’implémentation d’une solution d’estimation d’attitude.Celle-ci est basée sur un ensemble de filtres complémentaires combinés avec un algorithmealgébrique tel que TRIAD, QUEST, etc. avec la possibilité de choisir deux formes différentesdes filtres: la première dénommée forme Directe, et la seconde dénommée forme Passive.Les filtres proposés ont une flexibilité dans le choix de l’ordre qui peut être pris grand afinde bien réduire l’effet du bruit de mesure et permettent d’aboutir à un estimateur qui peutprendre en compte le biais éventuel des gyromètres. L’analyse par la théorie de Lyapunovprouve que les erreurs d’estimation tendent globalement et asymptotiquement vers zéro. Unesuite logique de cette première contribution est la proposition d’une solution pour la commanded’attitude qui constitue la deuxième contribution de cette thèse. Elle se traduit par ledéveloppement d’une nouvelle loi de commande d’attitude d’un corps rigide dans l’espace3D, dans laquelle seulement les vecteurs de mesures inertiels avec les mesures des gyromètressont utilisés. Elle utilise le principe de fusion des données à travers un filtre complémentairepermettant l’élimination des bruits des mesures tout en assurant une stabilité presque globalede l’équilibre désiré. La troisième contribution est une loi de commande pour la stabilisationd’attitude sans mesure de vitesse angulaire, ni mesure d’attitude. Pour cela, un systèmelinéaire auxiliaire basé sur les mesures des vecteurs inertiels a été introduit. Ce dernier sesubstitue au manque de l’information de la vitesse angulaire. L’analyse de stabilité du contrôleurproposé est basée sur la théorie de Lyapunov couplée avec le théorème de LaSalle. Ellepermet de conclure sur la stabilité presque globale de l’équilibre désiré. Les performances dessolutions proposées ont été validées par un ensemble de tests expérimentaux / Nowadays, we see a growing popularity of the use of Unmanned Aerial Vehicles (UAV) ofespecially Vertical Take-Off and Landing (VTOL) type. One of the most known VTOL is thequadrotor or Quadcopter which is probably the most used one as a research platform. Thisthesis deal with attitude control and estimation techniques applied to a rigid body movingin 3D space such as Quadcopter VTOL. The first contribution of this thesis is the design ofa new class of complementary linear-like filters allowing the fusion of inertial vector measurementswith angular velocity measurements and combined with algebraic algorithms asTRIAD, QUEST etc. to give an efficient attitude estimation solution. This class of filtersallows several possibilities of implementation such as the order of the filters which can bechosen high in order to reduce more the measurement noise and the form of the filters thatcan be direct or passive and the ability to take into account the possible gyro bias. Lyapunovanalysis shows the global asymptotic convergence of the estimation errors to zero. The sameprinciple of data fusion is used for the proposed new attitude control law in which the complementaryfilters were included to reduce the effect of measurement noise. The obtainedcontroller ensures almost global stability of the desired equilibrium point; it represents thesecond contribution of this thesis. The third contribution takes into consideration an interestingspecial case, where instantaneous measurements of attitude and angular velocity areunavailable. A first order linear auxiliary system based directly on vector measurements isused in an observer-like system to handle the luck of angular velocity. The proposed controllerensures almost global asymptotic stability of the trajectories to the desired equilibriumpoint. Detailed sets of experiments were done to validate the obtained results

This thesis describes the work done to implement and develop position and trajectory control of a quadcopter. The quadcopter was originally equipped with sensors and software to estimate and control the quadcopter's orientation, but did not estimate the current position. A GPS module, GPS antenna and a LIDAR have been added to measure the position in three dimensions. Filters have been implemented and developed to estimate the position, velocity and acceleration. Four controllers have been designed that use these estimates: one PID controller and one LQ controller for vertical movement, and a position controller and a trajectory controller for horizontal movement. The position controller maintains a constant position, while the trajectory controller maintains a constant velocity while travelling along a straight line. These position and trajectory controllers calculate the reference angles required to direct the thrust necessary to control the quadcopter's movement. Additionally, an algorithm has been developed to decrease overshoot by predicting future trajectories. These controllers have proven to be successful at controlling the quadcopter's position in all three dimensions, both in practice during outdoor flight and in simulations.

A drone is an unmanned aerial vehicle with multiple forms of usage. Drones can be programmed to fly with different degrees of autonomous flight. Autonomous controlled flight makes it possible for the drone to fly without human involvement and it is then controlled solely by software. The goal of this project is to program the micro drone Crazyflie 2.1 to autonomously fly through an obstacle course in the shortest amount of time and a predetermined direction. The nature and placement of the obstacles are unknown beforehand. The obstacles are detected and avoided by using the obstacle detection sensor Multi- ranger. To achieve autonomous flight two possible navigation systems were tested, the Loco Positioning System and Flow deck. Flying the Crazyflie while using Flow deck as positioning system performed best, managing to fly through the obstacle course avoiding all obstacles.

The subject of this master‘s thesis is the design of a passenger quadcopter, which is intended for transporting two passengers and luggage. Diploma thesis includes an analysis of the existing products and deals with further direction in the solved topic. The goal of this thesis is to create a conceptual design that respects the technical, ergonomic and aesthetic requirements. The proposal itself offers possible direction of autonomous passenger quadcopters in the future.

This thesis deals with the design and implementation of the electronic speed controller for a BLDC motor that will be used in a quadcopter. The first part of the thesis explains the methods of control and sensorless commutation respectively. The following part describes the design of the hardware controller. The control firmware was programmed and debugged for this controller.

Quadcopter drone formation control is an important capability for fields like area surveillance, search and rescue, agriculture, and reconnaissance. Of particular interest is formation control in environments where radio communications and/or GPS may be either denied or not sufficiently accurate for the desired application. To address this, we focus on vision as the sensing modality. We train an Hourglass Convolutional Neural Network (CNN) to discriminate between quadcopter pixels and non-quadcopter pixels in a live video feed and use it to guide a formation of quadcopters. The CNN outputs "heatmaps" - pixel-by-pixel likelihood estimates of the presence of a quadcopter. These heatmaps suffer from short-lived false detections. To mitigate these, we apply a version of the Siamese networks technique on consecutive frames for clutter mitigation and to promote temporal smoothness in the heatmaps. The heatmaps give an estimate of the range and bearing to the other quadcopter(s), which we use to calculate flight control commands and maintain the desired formation. We implement the algorithm on a single-board computer (ODROID XU4) with a standard webcam mounted to a quadcopter drone. Flight tests in a motion capture volume demonstrate successful formation control with two quadcopters in a leader-follower setup.

No description available.

The industry of UAVs has experienced a boost in recent years, and developments on both the hardware and algorithmic side have enabled smaller and more accessible drones with increased functionality. This thesis investigates the possibilities of autonomous target recognition and tracking in small, low-cost drones that are commercially available today. The design and deployment of an object recognition and tracking algorithm on a Crazyflie 2.1, a palm-sized quadcopter with a weight of a few tens of grams, is presented. The hardware is extended with an expansion board called the AI-deck featuring a fixed, front-facing camera and a GAP8 processor for machine learning inference. The aim is to create a vision-based autonomous control system for target recognition and following, with all computations being executed onboard and without any dependence on external input. A MobileNet-SSD object detector trained for detecting human bodies is used for detecting a person in images from the onboard camera. Proportional controllers are implemented for motion control of the Crazyflie, that process the output from the detection algorithm to move the drone to the desired position. The final implementation is tested indoors and proved to be able to detect a target and follow simple movements of a human moving in front of the drone. However, the reliability and speed of the detection need to be improved to achieve a satisfactory result.

No description available.

There are many possible ways to perform the attitude control of a quadcopter and, recently, the subject of event-triggered control has become relevant in the scientic community. This thesis deals with the analysis and implementation of a saturating attitude controller for a quadcopter system, together with the derivation of an event-triggering rule to work with it. Two distinct rules are presented, one that ensures the stability of the closed loop system, the other, a linearised version that does not. The way those were derived consists in the use of a Lyapunov based approach. The stability of the system when under these rules was veried experimentally.