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The attitude (or orientation) of an object is often crucial in its ability to perform a task, whether the task is driving a car, flying an aircraft, or focusing a satellite. In traditional control approaches, the attitude is often parameterized by Euler angles or unit quaternions which exhibit problems such as gimbal lock or ambiguity in representation, respectively. These complications prevent the controllers from achieving global stability and worse they may cause real physical harm due to unexpected large motions. More recent works have achieved global stability and avoided these system failures by working directly on the configuration manifold, but these approaches are generally complex or lack automatic, user-friendly ways to tune them. The goal of this dissertation is to develop simple geometric attitude controllers that are globally, exponentially stable and can be automatically tuned. By simple, we mean that the controllers are computationally efficient for real time implementation on embedded computers and the tuning parameters have geometric interpretations. These properties make the controllers user friendly and practical for real hardware implementation even on fast dynamical systems. Furthermore, we aim to obtain an automatic tuning procedure that ensures convergence, and can also quantify and optimize performance guarantees. We achieve our goal through four major contributions. The first is a substantial generalization on the theory of classical Riemannian metrics for tangent bundles which provides the ability to compare and combine attitude and velocity terms in the stability analysis, allowing us to consider a larger set of feasible controller gains. The second contribution is a framework to study the stability of attitude systems on manifolds and to automatically tune the controller gains by combining Riemannian geometry, contraction theory, and offline optimization. The third contribution is the development of a globally, exponentially stable attitude controller. This controller overcomes the topological limitation that prevents continuous, time-invariant controllers from achieving global stability by using a time-varying intermediate reference trajectory. The fourth contribution is the improvement of the proposed controllers by way of point-wise-in-time quadratic programming.

The quadrotor helicopter is a unique flying vehicle which uses the thrust from four motors to provide hover flight capability. The uncoupled nature of the longitudinal and lateral axes and its ability to support large payloads with respect to its size make it an attractive vehicle for autonomous vehicle research. In this thesis, the quadrotor is modeled based on first principles and a proportional-derivative control method is applied for attitude stabilization and position control. A unique means of using an optic flow sensor for velocity and position estimation in an indoor setting is presented with flight results. Reliable hover flight and hallway following capabilities are exhibited in GPS-denied indoor flight using only onboard sensors. Attitude angles can be reliably estimated in the short run by integrating the angular rates from MEMS gyros, but noise on the signal leads to drift which renders the measurement unsuitable to attitude estimation. Typical methods of providing vector attitude corrections such as accelerometers and magnetometers have inherent weaknesses on hovering vehicles. Thus, an additional vector measurement is necessary to correct attitude readings for long-term flights. Two methods of using image processing to determine vanishing points in a hallway are demonstrated. The more promising of the two uses a Hough transform to detect lines in the image and forms a histogram of the intersections to detect likely vanishing point candidates. Once the vanishing point is detected, it acts as a vector measurement to correct attitude estimates on the quadrotor vehicle. Results using onboard vision to estimate heading are demonstrated on a test stand. Together, these capabilities improve the utility of the quadrotor platform for flight without the need of any external sensing capability.

A quadrotor is an omnidirectional unmanned air vehicle that is suitable for indoor flight because of its ability to hover and maneuver in confined spaces. The remote operation of this type of vehicle is difficult due to a lack of sensory perception; typically, the view from the onboard camera is the only information transmitted to the pilot. This thesis proposes using force feedback exerted by the command input device on the hand of the pilot to assist in avoiding collisions while navigating in indoor environments. Five candidate algorithms are presented for calculating the forces to be felt by the pilot based on the quadrotor's position and velocity in the indoor environment. The candidates include a parametric algorithm based on the dynamics of the quadrotor, two time-to-impact algorithms, and two algorithms that employ virtual springs between the quadrotor and obstacles. A method of incorporating the position of the command input device to improve the usability and effectiveness of the algorithms is also presented. A framework for simulating the quadrotor dynamics, indoor environment, and force feedback algorithms is described. In the simulation, the pilot commands a simulated quadrotor, using a commercial haptic interface, as it flies in an indoor environment. The pilot receives force feedback cues as the quadrotor navigates around obstacles. Two methods of control were used for the simulation. In the first method, displacements of the haptic interface correspond to velocity commands to the quadrotor. In the second method, displacements of the input correspond to desired roll and pitch commands. Two user study experiments, one for each control method, were performed to compare the force feedback algorithms in simulation. The results of the velocity control experiment suggest that higher force levels help to avoid collisions and that the time to impact algorithm results in fewer collisions than having no force, but is not significantly better than the other algorithms. The results of the angle control experiment suggest that the time to impact algorithm is clearly the best in terms of hits and hit length and has no disadvantages compared to the other algorithms. Finally, to demonstrate the force feedback algorithms and software in a real-world environment, the system was interfaced with a physical quadrotor. The quadrotor system is described and the results of the tests are presented.

Autonomous flight in unstructured, confined, and unknown GPS-denied environments is a challenging problem. Solutions could be tremendously beneficial for scenarios that require information about areas that are difficult to access and that present a great amount of risk. The goal of this research is to develop a new framework that enables improved solutions to this problem and to validate the approach with experiments using a hardware prototype. In Chapter 2 we examine the consequences and practical aspects of using an improved dynamic model for multirotor state estimation, using only IMU measurements. The improved model correctly explains the measurements available from the accelerometers on a multirotor. We provide hardware results demonstrating the improved attitude, velocity and even position estimates that can be achieved through the use of this model. We propose a new architecture to simplify some of the challenges that constrain GPS-denied aerial flight in Chapter 3. At the core, the approach combines visual graph-SLAM with a multiplicative extended Kalman filter (MEKF). More importantly, we depart from the common practice of estimating global states and instead keep the position and yaw states of the MEKF relative to the current node in the map. This relative navigation approach provides a tremendous benefit compared to maintaining estimates with respect to a single global coordinate frame. We discuss the architecture of this new system and provide important details for each component. We verify the approach with goal-directed autonomous flight-test results. The MEKF is the basis of the new relative navigation approach and is detailed in Chapter 4. We derive the relative filter and show how the states must be augmented and marginalized each time a new node is declared. The relative estimation approach is verified using hardware flight test results accompanied by comparisons to motion capture truth. Additionally, flight results with estimates in the control loop are provided. We believe that the relative, vision-based framework described in this work is an important step in furthering the capabilities of indoor aerial navigation in confined, unknown environments. Current approaches incur challenging problems by requiring globally referenced states. Utilizinga relative approach allows more flexibility as the critical, real-time processes of localization and control do not depend on computationally-demanding optimization and loop-closure processes.

Drones have a new emerging use case: performing in shows and live events. This master thesis has been driven by an artistic project invited to take part in a full-scale operatic performance in the Croatian National Theatre Ivan Zajc in Rijeka, Croatia, in 2019. This project merges technological research with ancient theatrical and operatic traditions by using drones as an opera choir. After describing the process of designing and building a fleet of quadrotors equipped with speakers, we present a reacting and interacting motion planning strategy based on potential fields. We analyse and evaluate our drone design with its control strategy on simulation and on a real drone. / Droner har ett nytt framväxande användarfall: att delta i show- och liveevenemang. Detta examensarbete har drivits av ett konstnärligt projekt som inbjudits att delta i ett fullskaligt opera-uppträdande i den kroatiska nationalteatern Ivan Zajc i Rijeka, Kroatien, 2019. Detta projekt förenar teknisk forskning med gamla teatraliska och opera-traditioner genom att använda droner som en operakör. Efter att ha beskrivit processen att designa och bygga en flotta quadrotors utrustade med högtalare presenterar vi en reagerande och interaktiv rörelseplaneringsstrategi baserad på potentiella fält. Vi analyserar och utvärderar vår drone-design med sin kontrollstrategi för simulering och på en riktig drone.

The aim of this thesis is to advance the control and estimation schemes for multirotors, and more specifically the Aerial Robotic Worker, in order to progress towards the necessary control and estimation performance for robust control, cooperation and collaboration. Towards this envisioned aim, this Licentiate thesis will present the following main research contributions: a) a singularity-free attitude controller for the attitude problem has been established, that does not have the inherent drawbacks of Euler angle or Direction Cosine Matrix based approaches, b) a generalized estimation scheme for attitude, position and parameter estimation will be presented that has the merit of low computational footprint, while it is robust towards magnetic disturbances and able to identify key parameters in the model of an Aerial Robotic Worker, c) an method for estimating the induced vibration frequencies on the multirotor’s frame, and the respective amplitudes, that relies on notch filtering for attenuating the induced vibrations, and d) a theoretical establishment, as well as an experimental development and evaluation of a variable pitch propeller model to add additional degrees of freedom and increase the robustness of an Aerial Robotic Worker. In the first part of this thesis the main contributions of the previous research approaches will be highlighted, while in the second part of the thesis the corresponding and in full detail articles will be presented.

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