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.
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- Genre: Masters Thesis
- Creators: Redkar, Sangram
Description
In nearly all commercially successful internal combustion engine applications, the slider crank mechanism is used to convert the reciprocating motion of the piston into rotary motion. The hypocycloid mechanism, wherein the crankshaft is replaced with a novel gearing arrangement, is a viable alternative to the slider crank mechanism. The geared hypocycloid mechanism allows for linear motion of the connecting rod and provides a method for perfect balance with any number of cylinders including single cylinder applications. A variety of hypocycloid engine designs and research efforts have been undertaken and produced successful running prototypes. Wiseman Technologies, Inc provided one of these prototypes to this research effort. This two-cycle 30cc half crank hypocycloid engine has shown promise in several performance categories including balance and efficiency. To further investigate its potential a more thorough and scientific analysis was necessary and completed in this research effort. The major objective of the research effort was to critically evaluate and optimize the Wiseman prototype for maximum performance in balance, efficiency, and power output. A nearly identical slider crank engine was used extensively to establish baseline performance data and make comparisons. Specialized equipment and methods were designed and built to collect experimental data on both engines. Simulation and mathematical models validated by experimental data collection were used to better quantify performance improvements. Modifications to the Wiseman prototype engine improved balance by 20 to 50% (depending on direction) and increased peak power output by 24%.
ContributorsConner, Thomas (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Bradley (Committee member) / Georgeou, Trian (Committee member) / Arizona State University (Publisher)
Created2011
Description
The objective of this work is to develop a Stop-Rotor Multimode UAV. This UAV is capable of vertical take-off and landing like a helicopter and can convert from a helicopter mode to an airplane mode in mid-flight. Thus, this UAV can hover as a helicopter and achieve high mission range of an airplane. The stop-rotor concept implies that in mid-flight the lift generating helicopter rotor stops and rotates the blades into airplane wings. The thrust in airplane mode is then provided by a pusher propeller. The aircraft configuration presents unique challenges in flight dynamics, modeling and control. In this thesis a mathematical model along with the design and simulations of a hover control will be presented. In addition, the discussion of the performance in fixed-wing flight, and the autopilot architecture of the UAV will be presented. Also presented, are some experimental "conversion" results where the Stop-Rotor aircraft was dropped from a hot air balloon and performed a successful conversion from helicopter to airplane mode.
ContributorsVargas-Clara, Alvaro (Author) / Redkar, Sangram (Thesis advisor) / Macia, Narciso (Committee member) / Rajadas, John (Committee member) / Arizona State University (Publisher)
Created2011
Description
The objective of this thesis was to compare various approaches for classification of the `good' and `bad' parts via non-destructive resonance testing methods by collecting and analyzing experimental data in the frequency and time domains. A Laser Scanning Vibrometer was employed to measure vibrations samples in order to determine the spectral characteristics such as natural frequencies and amplitudes. Statistical pattern recognition tools such as Hilbert Huang, Fisher's Discriminant, and Neural Network were used to identify and classify the unknown samples whether they are defective or not. In this work, a Finite Element Analysis software packages (ANSYS 13.0 and NASTRAN NX8.0) was used to obtain estimates of resonance frequencies in `good' and `bad' samples. Furthermore, a system identification approach was used to generate Auto-Regressive-Moving Average with exogenous component, Box-Jenkins, and Output Error models from experimental data that can be used for classification
ContributorsJameel, Osama (Author) / Redkar, Sangram (Thesis advisor) / Arizona State University (Publisher)
Created2013
Description
In this work, we focused on the stability and reducibility of quasi-periodic systems. We examined the quasi-periodic linear Mathieu equation of the form x ̈+(ä+ϵ[cost+cosùt])x=0 The stability of solutions of Mathieu's equation as a function of parameter values (ä,ϵ) had been analyzed in this work. We used the Floquet type theory to generate stability diagrams which were used to determine the bounded regions of stability in the ä-ù plane for fixed ϵ. In the case of reducibility, we first applied the Lyapunov- Floquet (LF) transformation and modal transformation, which converted the linear part of the system into the Jordan form. Very importantly, quasi-periodic near-identity transformation was applied to reduce the system equations to a constant coefficient system by solving homological equations via harmonic balance. In this process we obtained the reducibility/resonance conditions that needed to be satisfied to convert a quasi-periodic system to a constant one.
ContributorsEzekiel, Evi (Author) / Redkar, Sangram (Thesis advisor) / Meitz, Robert (Committee member) / Nam, Changho (Committee member) / Arizona State University (Publisher)
Created2012
Description
Suction stabilized floats have been implemented into a variety of applications such as supporting wind turbines in off-shore wind farms and for stabilizing cargo ships. This thesis proposes an alternative use for the technology in creating a system of suction stabilized floats equipped with real time location modules to help first responders establish a localized coordinate system to assist in rescues. The floats create a stabilized platform for each anchor module due to the inverse slack tank effect established by the inner water chamber. The design of the float has also been proven to be stable in most cases of amplitudes and frequencies ranging from 0 to 100 except for when the frequency ranges from 23 to 60 Hz for almost all values of the amplitude. The modules in the system form a coordinate grid based off the anchors that can track the location of a tag module within the range of the system using ultra-wideband communications. This method of location identification allows responders to use the system in GPS denied environments. The system can be accessed through an Android app with Bluetooth communications in close ranges or through internet of things (IoT) using a module as a listener, a Raspberry Pi and an internet source. The system has proven to identify the location of the tag in moderate ranges with an approximate accuracy of the tag location being 15 cm.
ContributorsDye, Michaela (Author) / Redkar, Sangram (Thesis advisor) / Sugar, Thomas (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2020
Description
Trajectory forecasting is used in many fields such as vehicle future trajectory prediction, stock market price prediction, human motion prediction and so on. Also, robots having the capability to reason about human behavior is an important aspect in human robot interaction. In trajectory prediction with regards to human motion prediction, implicit learning and reproduction of human behavior is the major challenge. This work tries to compare some of the recent advances taking a phenomenological approach to trajectory prediction. \par The work is expected to mainly target on generating future events or trajectories based on the previous data observed across many time intervals. In particular, this work presents and compares machine learning models to generate various human handwriting trajectories. Although the behavior of every individual is unique, it is still possible to broadly generalize and learn the underlying human behavior from the current observations to predict future human writing trajectories. This enables the machine or the robot to generate future handwriting trajectories given an initial trajectory from the individual thus helping the person to fill up the rest of the letter or curve. This work tests and compares the performance of Conditional Variational Autoencoders and Sinusoidal Representation Network models on handwriting trajectory prediction and reconstruction.
ContributorsKota, Venkata Anil (Author) / Ben Amor, Hani (Thesis advisor) / Venkateswara, Hemanth Kumar Demakethepalli (Committee member) / Redkar, Sangram (Committee member) / Arizona State University (Publisher)
Created2021
Description
Reinforcement Learning(RL) algorithms have made a remarkable contribution in the eld of robotics and training human-like agents. On the other hand, Evolutionary Algorithms(EA) are not well explored and promoted to use in the robotics field. However, they have an excellent potential to perform well. In thesis work, various RL learning algorithms like Q-learning, Deep Deterministic Policy Gradient(DDPG), and Evolutionary Algorithms(EA) like Harmony Search Algorithm(HSA) are tested for a customized Penalty Kick Robot environment. The experiments are done with both discrete and continuous action space for a penalty kick agent. The main goal is to identify which algorithm suites best in which scenario. Furthermore, a goalkeeper agent is also introduced to block the ball from reaching the goal post using the multiagent learning algorithm.
ContributorsTrivedi, Maitry Ronakbhai (Author) / Amor, Heni Ben (Thesis advisor) / Redkar, Sangram (Thesis advisor) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2021
Description
Multiple robotic arms collaboration is to control multiple robotic arms to collaborate with each other to work on the same task. During the collaboration, theagent is required to avoid all possible collisions between each part of the robotic arms.
Thus, incentivizing collaboration and preventing collisions are the two principles which
are followed by the agent during the training process. Nowadays, more and more
applications, both in industry and daily lives, require at least two arms, instead of
requiring only a single arm. A dual-arm robot satisfies much more needs of different
types of tasks, such as folding clothes at home, making a hamburger in a grill or
picking and placing a product in a warehouse.
The applications done in this paper are all about object pushing. This thesis
focuses on how to train the agent to learn pushing an object away as far as possible.
Reinforcement Learning (RL), which is a type of Machine Learning (ML), is then
utilized in this paper to train the agent to generate optimal actions. Deep Deterministic
Policy Gradient (DDPG) and Hindsight Experience Replay (HER) are the two RL
methods used in this thesis.
ContributorsLin, Steve (Author) / Ben Amor, Hani (Thesis advisor) / Redkar, Sangram (Committee member) / Zhang, Yu (Committee member) / Arizona State University (Publisher)
Created2023
Description
In today’s modern world, industrial robots are utilized in hazardous working condi-tions across all industries, including the renewable energy industry. Robot control
systems and sensors receive and transmit information and data obtained from the
users. Over the last ten years, unmanned vehicles have developed into a subject of
interest for a variety of research institutions. Technology breakthroughs are redefin-
ing disaster relief, search-and-rescue(SAR) and salvage operations’ for aerial robotic
systems as well as terrestrial and marine ones. A team of collaborative robots is
required for the challenging environments, such as space construction, and disaster
relief. These robots will have to make trade-offs between mobility and capabilities
owing to cost, power, and size constraints. Task execution in numerous areas may de-
mand for robot collaboration in order to optimize team performance. An analysis of
collaborative Unmanned Aerial Vehicle(UAV) and Unmanned Ground Vehicle(UGV)
systems is one of the main components of this thesis. UAV/UGV collaborative frame-
works and methods have been presented for reaching or monitoring moving human
targets, a stated set-point for a mobile UGV robot to go to in order to approach
a dynamic target, and actions to take by the UAVs when the mobile UGV robot
is obstructed and cannot reach the target. This method encourages the target and
robot to work together more closely. This is one of the most difficult issues in search
and rescue operations since human targets are seldom found using just land robots or
aerial robots. Finally, the purpose of this thesis is to suggest that the evaluation of
the performance of a collaborative robot system may be accomplished by measuring
the mobility of robots. Even though multi-robot coordination aids in SAR opera-
tions, the findings of the study presented in this thesis conclude that the integration
of various autonomous robotic systems in unstructured environments is difficult and
that there is currently no unitary analytical model that can be used for this purpose.
ContributorsCherupally, SuryaKiran (Author) / Redkar, Sangram (Thesis advisor) / Nichols, Kevin (Committee member) / Subramanian, Susheel Kumar Cherangara (Committee member) / Arizona State University (Publisher)
Created2022
Description
Photoplethysmography (PPG) is currently a leading and growing field of researchwithin the biomedical industry. With its primary use in pulse oximetry and capability
of quickly, non-intrusively, evaluating essential vital signs like heart rate and
oxygen levels. This thesis will explore the literature on new and innovative research
in pulse oximetry. Then introduce PPG signals including how to calculate heart rate,
oxygen saturation, and current problems, mainly focused on motion artifacts. The
development of hardware and software systems using Bluetooth to transmit data to
MATLAB for algorithm processing. Testing different signal processing techniques
and parameters evaluating their effects on algorithm accuracy and reduction of motion
artifact. Using accelerometers to identify motion and apply filters to effectively
reduce minor motion artifacts. Then perform real-time data analysis and algorithm
processing resulting in heart rate and oxygen level calculations.
ContributorsMuhn, George (Author) / Redkar, Sangram (Thesis advisor) / Nichols, Kevin (Committee member) / Subramanian, Susheelkumar (Committee member) / Arizona State University (Publisher)
Created2022