Design and Evaluation of Novel Attitude Estimation System Using MEMS Sensors for Indoor UAS

Most small unmanned aerial systems in use today, employ extended Kalman filter sensor fusion algorithms in order to provide accurate estimations of attitude or orientation. These complex algorithms use measurements from GPS receivers and magnetometer sensors that can be rendered useless in GPS denied environments or areas of significant magnetic interference, such as inside buildings or other structures. The complexity of these algorithms makes them inaccessible for some researchers and hobbyists who wish to code their own attitude estimation algorithms. This complexity is also computationally expensive and requires processors that are powerful enough to operate the algorithms along with any command and control functions required by the application. In contrast, there are simple sensor fusion algorithms such as the complementary filter or linear Kalman filter, that are commonly used by hobbyists because they are relatively easy to implement and computationally lightweight. However, these methods are not as accurate as the extended Kalman filter and therefore, are not adequate for some of the emerging precision applications in aerial robotics. The goal of this research is to investigate an attitude estimation algorithm that uses two separate inertial measurement units (IMUs), each consisting of tri-axis accelerometers and tri-axis gyroscopes. This dual or twin IMU (TIMU) algorithm is compared to several common algorithms that only use one IMU, such as the complementary filter and linear Kalman filter. Analysis of a one degree of freedom experiment shows that the TIMU algorithm provides a more accurate attitude estimate. The analysis also shows that distance between the IMU and the rotating body’s center of gravity can have an inverse effect on attitude accuracy. The ability of the algorithms to provide an accurate estimate of the rate of attitude change is used as a performance metric, in addition to the accuracy of attitude estimates. The complexity of the twin IMU algorithm is kept to a minimum. It is presented in a way that can be easily programed by the layman and has a small computational footprint

Adaptive filtering algorithms for quaternion-valued signals

Advances in sensor technology have made possible the recoding of three and four-dimensional signals which afford a better representation of our actual three-dimensional world than the ``flat view'' one and two-dimensional approaches. Although it is straightforward to model such signals as real-valued vectors, many applications require unambiguous modeling of orientation and rotation, where the division algebra of quaternions provides crucial advantages over real-valued vector approaches. The focus of this thesis is on the use of recent advances in quaternion-valued signal processing, such as the quaternion augmented statistics, widely-linear modeling, and the HR-calculus, in order to develop practical adaptive signal processing algorithms in the quaternion domain which deal with the notion of phase and frequency in a compact and physically meaningful way. To this end, first a real-time tracker of quaternion impropriety is developed, which allows for choosing between strictly linear and widely-linear quaternion-valued signal processing algorithms in real-time, in order to reduce computational complexity where appropriate. This is followed by the strictly linear and widely-linear quaternion least mean phase algorithms that are developed for phase-only estimation in the quaternion domain, which is accompanied by both quantitative performance assessment and physical interpretation of operations. Next, the practical application of state space modeling of three-phase power signals in smart grid management and control systems is considered, and a robust complex-valued state space model for frequency estimation in three-phase systems is presented. Its advantages over other available estimators are demonstrated both in an analytical sense and through simulations. The concept is then expanded to the quaternion setting in order to make possible the simultaneous estimation of the system frequency and its voltage phasors. Furthermore, a distributed quaternion Kalman filtering algorithm is developed for frequency estimation over power distribution networks and collaborative target tracking. Finally, statistics of stable quaternion-valued random variables, that include quaternion-valued Gaussian random variables as a special case, is investigated in order to develop a framework for the modeling and processing of heavy-tailed quaternion-valued signals.Open Acces

Rigid Body Attitude Estimation: An Overview and Comparative Study

The attitude estimation of rigid body systems has attracted the attention of many researchers over the years. The development of efficient estimation algorithms that can accurately estimate the orientation of a rigid body is a crucial step towards a reliable implementation of control schemes for underwater and flying vehicles. The primary focus of this thesis consists in investigating various attitude estimation techniques and their applications. Two major classes are discussed. The first class consists of the earliest static attitude determination techniques relying solely on a set of body vector measurements of known vectors in the inertial frame. The second class consists of dynamic attitude estimation and filtering techniques, relying on body vector measurements as well other measurements, and using the dynamical equations of the system under consideration. Various attitude estimation algorithms, including the latest nonlinear attitude observers, are presented and discussed, providing a survey that covers the evolution and structural differences of these estimation methods. Simulation results have been carried out for a selected number of such attitude estimators. Their performance in the presence of noisy measurements, as well as their advantages and disadvantages are discussed

Design and Development of a High-Performance Quadrotor Control Architecture Based on Feedback Linearization

The purpose of this thesis is to outline the development of a high-performance quadrotor control system for an AscTec Hummingbird quadrotor using direct motor speed control within a Vicon motion capture system environment. A Ground Control Station (GCS) acts as a user interface for selecting flight patterns and displaying sensor values. An on-board Intel Edison embedded Linux computer acts as the quadrotor\u27s controller. The Vicon system measures the quadrotor\u27s position and orientation, while the Hummingbird\u27s stock AscTec Autopilot board provides inertial measurements and receives motor speed commands. Based on the flight pattern set by the GCS, smooth and di erentiable trajectories are generated. A control program was written for the Edison to obtain measurements, receive flight pattern commands, perform state estimation, calculate control laws, send motor speed commands to the Autopilot board, and log values. The program was written as a multithreaded C++ program for increased performance. A feedback linearization of the quadrotor\u27s dynamics was performed to account for its nonlinearities. A controller structure designed to ensure exponential Lyapunov stability was applied to the input-output linearized dynamics. The simplex method was used to aid the controller in pushing the Hummingbird\u27s actuators for aggressive maneuvers within set input limitations. The Edison\u27s Wi-Fi capabilities enable it to contact the Vicon server directly for position and orientation measurements. Accelerations and angular velocities are measured by the Autopilot\u27s inertial measurement unit (IMU). A quick state estimation process was implemented to filter the measured states, and state prediction was used to compensate for latency in the system. A custom circuit board and communication framework was designed and assembled for interfacing the Edison with the Autopilot. The custom communication framework allowed for a 16 times speed improvement over the default settings while bypassing the stock wireless communication\u27s inherently unreliable timing. The Hummingbird\u27s physical properties, such as propeller performance and rotational inertias, were characterized via static and step response experiments. The control system\u27s flight performance was evaluated through simulation and experimental tests

Two dimensional rate gyro bias estimation for precise pitch and roll attitude determination utilizing a dual arc accelerometer array

In this thesis, a previously developed, novel one-dimensional attitude estimation device is expanded through the development and implementation of an innovative method for estimation of two-dimensional attitude making use of a unique low-cost, dual arc accelerometer array measuring longitudinal and transverse rotational rates in real-time. The device and method proposed is an expansion of a previously developed method for one-dimensional attitude determination and rate gyro bias estimation utilizing a one-dimensional accelerometer array. This new revolutionary device utilizes a dual arc accelerometer array and an algorithm for accurate and reliable two-dimensional attitude determination and rate gyro bias estimation in real-time. The method determines the local gravitational field vector from which attitude information can be resolved. Upon determining the location of the local gravitational field vector relative to two consecutive accelerometer sensors, the orientation of the device may then be estimated and the attitude determined. However, this measurement is discrete in nature; therefore, integrated rate gyro measurements are used to determine attitude information resulting in a continuous signal. However, attitude estimates and measurements produced by instantaneous rate sensors and gyroscope integration tend to drift over time due to drift and bias inherent to the rate gyro sensor. The integration of the acquired instantaneous rate signals amplify measurement errors leading to an undependable and imprecise estimate of the vehicles true attitude and orientation. A method for compensation of these errors is proposed in this work resulting in a highly accurate and continuous attitude estimate. For this thesis, simulations of the proposed method and device will be conducted with the inclusion of characteristic, real-world sensor noise and bias estimates produced from corrupted and biased sensors to analyze and assess the feasibility and validity of the proposed method and system configuration for two-dimensional attitude determination. The end goal of this work is to produce a precise and reliable longitudinal and transverse attitude estimation array capable of measuring rate senor and gyro bias online so as to produce highly accurate and reliable pitch and roll angle tracking in real-time while under subjection to simulated flight conditions and scenarios. While this thesis is an expansion of a previously developed device and method, it is a departure from past works in that a new, two-dimensional accelerometer array arc is utilized and additional rotational dimensions are being included in the simulated analysis