This dissertation aims to apply non-zero attitude and position setpoint tracking to a quadrotor aircraft, achieved by solving the problem of a quadrotor’s inherent underactuation. The introduction of extra actuation aims to mechanically accommodate for stable tracking of non-zero state trajectories. The requirement of the project is to design, model, simulate and control a novel quadrotor platform which can articulate all six degrees of rotational and translational freedom (6-DOF) by redirecting and vectoring each propeller’s individually produced thrust. Considering the extended articulation, the proposal is to add an additional two axes (degrees) of actuation to each propeller on a traditional quadrotor frame. Each lift propeller can be independently pitched or rolled relative to the body frame. Such an adaptation, to what is an otherwise well understood aircraft, produces an over-actuated control problem. Being first and foremost a control engineering project, the focus of this work is plant model identification and control solution of the proposed aircraft design. A higher-level setpoint tracking control loop designs a generalized plant input (net forces and torques) to act on the vehicle. An allocation rule then distributes that virtual input in solving for explicit actuator servo positions and rotational propeller speeds. The dissertation is structured as follows: First a schedule of relevant existing works is reviewed in Ch:1 following an introduction to the project. Thereafter the prototype’s design is detailed in Ch:2, however only the final outcome of the design stage is presented. Following that, kinematics associated with generalized rigid body motion are derived in Ch:3 and subsequently expanded to incorporate any aerodynamic and multibody nonlinearities which may arise as a result of the aircraft’s configuration (changes). Higher-level state tracking control design is applied in Ch:4 whilst lower-level control allocation rules are then proposed in Ch:5. Next, a comprehensive simulation is constructed in Ch:6, based on the plant dynamics derived in order to test and compare the proposed controller techniques. Finally a conclusion on the design(s) proposed and results achieved is presented in Ch:7. Throughout the research, physical tests and simulations are used to corroborate proposed models or theorems. It was decided to omit flight tests of the platform due to time constraints, those aspects of the project remain open to further investigation. The subsequent embedded systems design stemming from the proposed control plant is outlined in the latter of Ch:2, Sec:2.4. Such implementations are not investigated here but design proposals are suggested. The primary outcome of the investigation is ascertaining the practicality and feasibility of such a design, most importantly whether or not the complexity of the mechanical design is an acceptable compromise for the additional degrees of control actuation introduced. Control derivations and the prototype design presented here are by no means optimal nor the most exhaustive solutions, focus is placed on the whole system and not just a single aspect of it.

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La vision représente un des plus importants signaux en robotique. Une caméra monoculaire peut fournir de riches informations visuelles à une fréquence raisonnable pouvant être utilisées pour la commande, l’estimation d’état ou la navigation dans des environnements inconnus par exemple. Il est cependant nécessaire de respecter des contraintes visuelles spécifiques telles que la visibilité de mesures images et les occultations durant le mouvement afin de garder certaines cibles visuelles dans le champ de vision. Les quadrirotors sont dotés de capacités de mouvement très réactives du fait de leur structure compacte et de la configuration des moteurs. De plus, la vision par une caméra embarquée (fixe) va subir des rotations dues au sous-actionnement du système. Dans cette thèsenous voulons bénéficier de l’agilité du quadrirotor pour réaliser plusieurs tâches de navigation basées vision. Nous supposons que l’estimation d’état repose uniquement sur la fusion capteurs d’une centrale inertielle (IMU) et d’une caméra monoculaire qui fournit des estimations de pose précises. Les contraintes visuelles sont donc critiques et difficiles dans un tel contexte. Dans cette thèse nous exploitons l’optimisation numérique pour générer des trajectoires faisables satisfaisant un certain nombre de contraintes d’état, d’entrées et visuelles non linéaires. A l’aide la platitude différentielle et de la paramétrisation par des B-splines nous proposons une stratégie de replanification performante inspirée de la commande prédictive pour générer des trajectoires lisses et agiles. Enfin, nous présentons un algorithme de planification en temps minimum qui supporte des pertes de visibilité intermittentes afin de naviguer dans des environnements encombrés plus vastes. Cette contribution porte l’incertitude de l’estimation d’état au niveau de la planification pour produire des trajectoires robustes et sûres. Les développements théoriques discutés dans cette thèse sont corroborés par des simulations et expériences en utilisant un quadrirotor. Les résultats reportés montrent l’efficacité des techniques proposées. / Vision constitutes one of the most important cues in robotics. A single monocular camera can provide rich visual information at a reasonable rate that can be used as a feedback for control, state estimation of mobile robots or safe navigation in unknown environments for instance. However, it is necessary to satisfy particular visual constraints on the image such as visibility and occlusion constraints during motion to keep some visual targets visible. Quadrotors are endowed with very reactive motion capabilities due to their compact structure and motor configuration. Moreover, vision from a (fixed) on-board camera will suffer from rotation motions due to the system underactuation. In this thesis, we want to benefit from the system aggressiveness to perform several vision-based navigation tasks. We assume state estimation relies solely on sensor fusion of an onboard inertial measurement unit (IMU) and a monocular camera that provides reliable pose estimates. Therefore, visual constraints are challenging and critical in this context. In this thesis we exploit numerical optimization to design feasible trajectories satisfying several state, input and visual nonlinear constraints. With the help of differential flatness and B-spline parametrization we will propose an efficient replanning strategy inspired form Model Predictive Control to generate smooth and agile trajectories. Finally, we propose a minimum-time planning algorithm that handles intermittent visibility losses in order to navigate in larger cluttered environments. This contribution brings state estimation uncertainty at the planning stage to produce robust and safe trajectories. All the theoretical developments discussed in this thesis are corroborated by simulations and experiments run by using a quadrotor UAV. The reported results show the effectiveness of proposed techniques.