UMD Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/3

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.

More information is available at Theses and Dissertations at University of Maryland Libraries.

Browse

Subject: search.filters.subject.Autonomous

Search Results

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Development of Low-Cost Autonomous Systems
    (2023) Saar, Logan Miles; Takeuchi, Ichiro; Material Science and Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    A central challenge of materials discovery for improved technologies arises from the increasing compositional, processing, and structural complexity involved when synthesizing hitherto unexplored material systems. Traditional Edisonian and combinatorial high-throughput methods have not been able to keep up with the exponential growth in potential materials and relevant property metrics. Autonomously operated Self-Driving Labs (SDLs) - guided by the optimal experiment design sub-field of machine learning, known as active learning - have arisen as promising candidates for intelligently searching these high-dimensional search spaces. In the fields of biology, pharmacology, and chemistry, these SDLs have allowed for expedited experimental discovery of new drugs, catalysts, and more. However, in material science, highly specialized workflows and bespoke robotics have limited the impact of SDLs and contributed to their exorbitant costs. In order to equip the next generation workforce of scientists and advanced manufacturers with the skills needed to coexist with, improve, and understand the benefits and limitations of these autonomous systems, a low-cost and modular SDL must be available to them. This thesis describes the development of such a system and its implementation in an undergraduate and graduate machine learning for materials science course. The low-cost SDL system developed is shown to be affordable for primary through graduate level adoption, and provides a hands-on method for simultaneously teaching active learning, robotics, measurement science, programming, and teamwork: all necessary skills for an autonomous compatible workforce. A novel hypothesis generation and validation active learning scheme is also demonstrated in the discovery of simple composition/acidity relationships.
  • Thumbnail Image
    Item
    An Observer for Estimating Translational Velocity from Optic Flow and Radar
    (2011) Gerardi, Steven Anthony; Humbert, James S; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    This thesis presents the development of a discrete time observer for estimating state information from optic flow and radar measurements. It is shown that estimates of translational and rotational speed can be extracted using a least squares inversion for wide fields of view or, with the addition of a Kalman Filter, for small fields of view. The approach is demonstrated in a simulated three dimensional urban environment on an autonomous quadrotor micro-air-vehicle (MAV). A state feedback control scheme is designed, whereby the gains are found via static H∞, and implemented to allow trajectory following. The proposed state estimation scheme and feedback method are shown to be sufficient for enabling autonomous navigation of an MAV. The resulting methodology has the advantages of computational speed and simplicity, both of which are imperative for implementation on MAVs due to stringent size, weight, and power requirements.
  • Thumbnail Image
    Item
    DESIGN OF AVIONICS AND CONTROLLERS FOR AUTONOMOUS TAKEOFF, HOVER AND LANDING OF A MINI-TANDEM HELICOPTER
    (2010) Rahman, Shafiq Ur; Blankenship, Gilmer; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Robotics autonomy is an active research area these days and promises very useful applications. A lot of research has been carried out on Vertical Takeoff and Landing (VTOL) vehicles especially single rotor small scale helicopters. This thesis focuses on a small scale twin rotor helicopter. These helicopters are more useful because of their power efficiency, scalability, long range of center of gravity, shorter blades and most importantly their "all lift" feature. By "all lift" we mean that unlike single rotor helicopters where tail rotor's power is wasted just to cancel the torque of the main rotor both of its rotors are used for generating lift. This makes twin rotors ideal for lifting heavy weights. This thesis considers avionics systems and the controllers development for a twin rotor. It involves electronic component selection and integration, software development, system identification and design of zero rate compensators. The compensators designed are responsible for autonomous take-off, hover and landing of this unmanned aerial vehicle (UAV). Both time and frequency domain system identification approaches were evaluated and a selection was made based on hardware limitations. A systematic approach is developed to demonstrate that a rapid prototyping UAV can be designed from cheap off-the-shelf components that are readily available and functionally compatible. At the end some modifications to existing mechanical structure are proposed for more robust outdoor hovering.
  • Thumbnail Image
    Item
    Stochastic Properties of Wide Field Integrated Optic Flow Measurements
    (2009) Owen, Scott; Humbert, James S; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Wide Field Integration (WFI) is a biologically inspired method of spatially decomposing optic flow estimates to extract relevant behavioral cues for navigation. In this thesis, a framework is developed that allows the direct application of a Kalman filter to improve the state information extracted from optic flow measurements. In addition, the noise properties of optic flow measurements are characterized, and an architecture to propagate the uncertainty in optic flow measurements to WFI state estimates is formalized. The closed-loop performance of a ground robot maneuvering in a straight tunnel using WFI outputs is then analyzed using three different algorithms to compute optic flow. The performance of the robot is characterized by its ability to track the tunnel centerline, and the accuracy of the WFI state estimates are compared with the true state estimates using a visual motion capture system. Lastly, the Kalman filter is implemented on a ground robot and the modified closed-loop performance is analyzed.