ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
Displaying 1 - 2 of 2
Filtering by
- All Subjects: deep learning
- All Subjects: differential-drive
Description
Toward the ambitious long-term goal of a fleet of cooperating Flexible Autonomous Machines operating in an uncertain Environment (FAME), this thesis addresses several
critical modeling, design and control objectives for ground vehicles. One central objective was to show how off-the-shelf (low-cost) remote-control (RC) “toy” vehicles can be converted into intelligent multi-capability robotic-platforms for conducting FAME research. This is shown for two vehicle classes: (1) six differential-drive (DD) RC vehicles called Thunder Tumbler (DDTT) and (2) one rear-wheel drive (RWD) RC car called Ford F-150 (1:14 scale). Each DDTT-vehicle was augmented to provide a substantive suite of capabilities as summarized below (It should be noted, however, that only one DDTT-vehicle was augmented with an inertial measurement unit (IMU) and 2.4 GHz RC capability): (1) magnetic wheel-encoders/IMU for(dead-reckoning-based) inner-loop speed-control and outer-loop position-directional-control, (2) Arduino Uno microcontroller-board for encoder-based inner-loop speed-control and encoder-IMU-ultrasound-based outer-loop cruise-position-directional-separation-control, (3) Arduino motor-shield for inner-loop motor-speed-control, (4)Raspberry Pi II computer-board for demanding outer-loop vision-based cruise- position-directional-control, (5) Raspberry Pi 5MP camera for outer-loop cruise-position-directional-control (exploiting WiFi to send video back to laptop), (6) forward-pointing ultrasonic distance/rangefinder sensor for outer-loop separation-control, and (7) 2.4 GHz spread-spectrum RC capability to replace original 27/49 MHz RC. Each “enhanced”/ augmented DDTT-vehicle costs less than 175 but offers the capability of commercially available vehicles costing over 500. Both the Arduino and Raspberry are low-cost, well-supported (software wise) and easy-to-use. For the vehicle classes considered (i.e. DD, RWD), both kinematic and dynamical (planar xy) models are examined. Suitable nonlinear/linear-models are used to develop inner/outer-loopcontrol laws.
All demonstrations presented involve enhanced DDTT-vehicles; one the F-150; one a quadrotor. The following summarizes key hardware demonstrations: (1) cruise-control along line, (2) position-control along line (3) position-control along curve (4) planar (xy) Cartesian stabilization, (5) cruise-control along jagged line/curve, (6) vehicle-target spacing-control, (7) multi-robot spacing-control along line/curve, (8) tracking slowly-moving remote-controlled quadrotor, (9) avoiding obstacle while moving toward target, (10) RC F-150 followed by DDTT-vehicle. Hardware data/video is compared with, and corroborated by, model-based simulations. In short, many capabilities that are critical for reaching the longer-term FAME goal are demonstrated.
critical modeling, design and control objectives for ground vehicles. One central objective was to show how off-the-shelf (low-cost) remote-control (RC) “toy” vehicles can be converted into intelligent multi-capability robotic-platforms for conducting FAME research. This is shown for two vehicle classes: (1) six differential-drive (DD) RC vehicles called Thunder Tumbler (DDTT) and (2) one rear-wheel drive (RWD) RC car called Ford F-150 (1:14 scale). Each DDTT-vehicle was augmented to provide a substantive suite of capabilities as summarized below (It should be noted, however, that only one DDTT-vehicle was augmented with an inertial measurement unit (IMU) and 2.4 GHz RC capability): (1) magnetic wheel-encoders/IMU for(dead-reckoning-based) inner-loop speed-control and outer-loop position-directional-control, (2) Arduino Uno microcontroller-board for encoder-based inner-loop speed-control and encoder-IMU-ultrasound-based outer-loop cruise-position-directional-separation-control, (3) Arduino motor-shield for inner-loop motor-speed-control, (4)Raspberry Pi II computer-board for demanding outer-loop vision-based cruise- position-directional-control, (5) Raspberry Pi 5MP camera for outer-loop cruise-position-directional-control (exploiting WiFi to send video back to laptop), (6) forward-pointing ultrasonic distance/rangefinder sensor for outer-loop separation-control, and (7) 2.4 GHz spread-spectrum RC capability to replace original 27/49 MHz RC. Each “enhanced”/ augmented DDTT-vehicle costs less than 175 but offers the capability of commercially available vehicles costing over 500. Both the Arduino and Raspberry are low-cost, well-supported (software wise) and easy-to-use. For the vehicle classes considered (i.e. DD, RWD), both kinematic and dynamical (planar xy) models are examined. Suitable nonlinear/linear-models are used to develop inner/outer-loopcontrol laws.
All demonstrations presented involve enhanced DDTT-vehicles; one the F-150; one a quadrotor. The following summarizes key hardware demonstrations: (1) cruise-control along line, (2) position-control along line (3) position-control along curve (4) planar (xy) Cartesian stabilization, (5) cruise-control along jagged line/curve, (6) vehicle-target spacing-control, (7) multi-robot spacing-control along line/curve, (8) tracking slowly-moving remote-controlled quadrotor, (9) avoiding obstacle while moving toward target, (10) RC F-150 followed by DDTT-vehicle. Hardware data/video is compared with, and corroborated by, model-based simulations. In short, many capabilities that are critical for reaching the longer-term FAME goal are demonstrated.
ContributorsLin, Zhenyu (Author) / Rodriguez, Armando Antonio (Committee member) / Si, Jennie (Committee member) / Berman, Spring Melody (Committee member) / Arizona State University (Publisher)
Created2015
Description
This work is concerned with how best to reconstruct images from limited angle tomographic measurements. An introduction to tomography and to limited angle tomography will be provided and a brief overview of the many fields to which this work may contribute is given.
The traditional tomographic image reconstruction approach involves Fourier domain representations. The classic Filtered Back Projection algorithm will be discussed and used for comparison throughout the work. Bayesian statistics and information entropy considerations will be described. The Maximum Entropy reconstruction method will be derived and its performance in limited angular measurement scenarios will be examined.
Many new approaches become available once the reconstruction problem is placed within an algebraic form of Ax=b in which the measurement geometry and instrument response are defined as the matrix A, the measured object as the column vector x, and the resulting measurements by b. It is straightforward to invert A. However, for the limited angle measurement scenarios of interest in this work, the inversion is highly underconstrained and has an infinite number of possible solutions x consistent with the measurements b in a high dimensional space.
The algebraic formulation leads to the need for high performing regularization approaches which add constraints based on prior information of what is being measured. These are constraints beyond the measurement matrix A added with the goal of selecting the best image from this vast uncertainty space. It is well established within this work that developing satisfactory regularization techniques is all but impossible except for the simplest pathological cases. There is a need to capture the "character" of the objects being measured.
The novel result of this effort will be in developing a reconstruction approach that will match whatever reconstruction approach has proven best for the types of objects being measured given full angular coverage. However, when confronted with limited angle tomographic situations or early in a series of measurements, the approach will rely on a prior understanding of the "character" of the objects measured. This understanding will be learned by a parallel Deep Neural Network from examples.
The traditional tomographic image reconstruction approach involves Fourier domain representations. The classic Filtered Back Projection algorithm will be discussed and used for comparison throughout the work. Bayesian statistics and information entropy considerations will be described. The Maximum Entropy reconstruction method will be derived and its performance in limited angular measurement scenarios will be examined.
Many new approaches become available once the reconstruction problem is placed within an algebraic form of Ax=b in which the measurement geometry and instrument response are defined as the matrix A, the measured object as the column vector x, and the resulting measurements by b. It is straightforward to invert A. However, for the limited angle measurement scenarios of interest in this work, the inversion is highly underconstrained and has an infinite number of possible solutions x consistent with the measurements b in a high dimensional space.
The algebraic formulation leads to the need for high performing regularization approaches which add constraints based on prior information of what is being measured. These are constraints beyond the measurement matrix A added with the goal of selecting the best image from this vast uncertainty space. It is well established within this work that developing satisfactory regularization techniques is all but impossible except for the simplest pathological cases. There is a need to capture the "character" of the objects being measured.
The novel result of this effort will be in developing a reconstruction approach that will match whatever reconstruction approach has proven best for the types of objects being measured given full angular coverage. However, when confronted with limited angle tomographic situations or early in a series of measurements, the approach will rely on a prior understanding of the "character" of the objects measured. This understanding will be learned by a parallel Deep Neural Network from examples.
ContributorsDallmann, Nicholas A. (Author) / Tsakalis, Konstantinos (Thesis advisor) / Hardgrove, Craig (Committee member) / Rodriguez, Armando (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2020