Matching Items (429)
Filtering by
- All Subjects: engineering
- All Subjects: robotics
- Genre: Masters Thesis
Description
Micro-Electro Mechanical System (MEMS) is the micro-scale technology applying on various fields. Traditional testing strategy of MEMS requires physical stimulus, which leads to high cost specified equipment. Also there are a large number of wafer-level measurements for MEMS. A method of estimation calibration coefficient only by electrical stimulus based wafer level measurements is included in the thesis. Moreover, a statistical technique is introduced that can reduce the number of wafer level measurements, meanwhile obtaining an accurate estimate of unmeasured parameters. To improve estimation accuracy, outlier analysis is the effective technique and merged in the test flow. Besides, an algorithm for optimizing test set is included, also providing numerical estimated prediction error.
ContributorsDeng, Lingfei (Author) / Ozev, Sule (Thesis advisor) / Yu, Hongyu (Committee member) / Christen, Jennifer Blain (Committee member) / Arizona State University (Publisher)
Created2012
Description
As the explorations beyond the Earth's boundaries continue to evolve, researchers and engineers strive to develop versatile technologies capable of adapting to unknown space conditions. For instance, the utilization of Screw-Propelled Vehicles (SPVs) and robotics that utilize helical screws propulsion to transverse planetary bodies is a growing area of interest. An example of such technology is the Extant Exobiology Life Surveyor (EELS), a snake-like robot currently developed by the NASA Jet Propulsion Laboratory (JPL) to explore the surface of Saturn’s moon, Enceladus. However, the utilization of such a mechanism requires a deep and thorough understanding of screw mobility in uncertain conditions. The main approach to exploring screw dynamics and optimal design involves the utilization of Discrete Element Method (DEM) simulations to assess interactions and behavior of screws when interacting with granular terrains. In this investigation, the Simplified Johnson-Kendall-Roberts (SJKR) model is implemented into the utilized simulation environment to account for cohesion effects similar to what is experienced on celestial bodies like Enceladus. The model is verified and validated through experimental and theoretical testing. Subsequently, the performance characteristics of screws are explored under varying parameters, such as thread depth, number of screw starts, and the material’s cohesion level. The study has examined significant relationships between the parameters under investigation and their influence on the screw performance.
ContributorsAbdelrahim, Mohammad (Author) / Marvi, Hamid (Thesis advisor) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Arizona State University (Publisher)
Created2023
Description
The need for robust verification and validation of automated vehicles (AVs) to ensure driving safety grows more urgent as increasing numbers of AVs are allowed to operate on open roads. To address this need, AV developers can present a safety case to regulators and the public that provides an evidence-based justification of their assertion that an AV is safe to operate on open roads. This work aims to describe the development of a scenario-based testing methodology that contributes to this safety case. A high-level definition of this test selection and scoring methodology (TSSM) is first presented, along with an outline of its scope and key ideas. This is followed by a literature review that details the current state of the art in AV testing, including the driving performance metrics and equations that provide a basis for the TSSM. A chart-based method for quantifying an AV’s operational design domain (ODD) and behavioral competency portfolio is then described that provides the foundation for a scenario generation and filtration process. After outlining a method for the AV to progress through increasingly robust test methods based on its current technology readiness level (TRL), the generation and filtration of two sets of scenarios by the TSSM is outlined: a standardized set that can be used to compare the performance of vehicles with identical ODD and behavioral competency portfolios, and a set containing high-relevance scenarios that is partially randomized to ensure test integrity. A related framework for incorporating testing on open roads is subsequently specified. An equation for an overall AV driving performance score is then defined that quantifies the aggregate performance of the AV across all generated scenarios. The TSSM continues according to an iterative process, which includes a method for exploring edge and corner scenarios, until a stopping condition is achieved. Two proofs of concept are provided: a demonstration of the ability of the TSSM to pare scenarios from a preexisting database, and an example ODD and behavioral competency portfolio specification form. Finally, this work concludes by evaluating the TSSM and its proofs of concept and outlining possible future work on the methodology.
ContributorsO'Malley, Gavin (Author) / Wishart, Jeffrey (Thesis advisor) / Zhao, Junfeng (Thesis advisor) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2023
Description
Animals have always been a source of inspiration for real-life problems. The octopus is one such animal that has a lot of untapped potential. The octopus’s arm is without solid joints or bone structure and despite this it can achieve many complicated movements with virtually infinite degrees of freedom. This ability is made possible through the unique morphology of the arm. The octopus’s arm is divided into transverse, longitudinal, oblique, and circular muscle groups and each one has a unique muscle fiber orientation. The octopus’s arm is classified as a hydrostat because it maintains a constant volume while contracting with the help of its different muscle groups. These muscle groups allow elongation, shortening, bending, and twisting of the arm when they work in combination with each other. To confirm the role of transverse and longitudinal muscle groups, an electromyography (EMG) recording of these muscle groups was performed while an amputated arm of an Octopus bimaculoides was stimulated with an electrical signal to induce movement. Statistical analysis was performed on these results to confirm the roles of each muscle group quantitatively. Octopus arm morphology was previously assumed to be uniform along the arm. Through a magnetic resonance imaging (MRI) study at the proximal, middle, and distal sections of the arm this notion was disproven, and a new pattern was discovered. Drawing inspiration from this finding and previous octopus arm prototypes, 4 bio-inspired designs were conceived and tested in finite element analysis (FEA) simulations. Four tests in elongation, shortening, bending, and transverse-assisted bending movements were performed on all designs to compare each design’s performance. The findings in this study have applications in engineering and soft robotics fields for use cases such as, handling fragile objects, minimally invasive surgeries, difficult-to-access areas that require squeezing through small holes, and other novel cases.
ContributorsAhmadi, Salaheddin (Author) / Marvi, Hamidreza (Thesis advisor) / Fisher, Rebecca (Committee member) / Xu, Zhe (Committee member) / Arizona State University (Publisher)
Created2023
Description
Obesity has consistently presented a significant challenge, with excess body fat contributing to the development of numerous severe conditions such as diabetes, cardiovascular disease, cancer, and various musculoskeletal disorders. In this study, different methods are proposed to study substrate utilization (carbohydrates, proteins, and fats) in the human body and validate the biomarkers enabling to investigation of weight management and monitor metabolic health. The first technique to study was Indirect calorimetry, which assessed Resting Energy Expenditure (REE) and measured parameters like oxygen consumption (VO2) and carbon dioxide production (VCO2). A validation study was conducted to study the effectiveness of the medical device Breezing Med determining REE, VO2, and VCO2. The results were compared with correlation slopes and regression coefficients close to 1. Indirect Calorimetry can be used to determine carbohydrate and fat utilization but it requires additional correction for protein utilization. Protein utilization can be studied by analyzing urinary nitrogen. Therefore, a secondary technique was studied for identifying urea and ammonia concentration in human urine samples. Along this line two methods for detecting urea were explored, a colorimetric technique and it was validated against the Ion-Selective method. The results were then compared by correlation analysis of urine samples measured with both methods simultaneously curves. The equations for fat, carb, and protein oxidation, involving VO2, VCO2 consumption, and urinary nitrogen were implemented and validated, using the above-described methods in a human subject study with 16 subjects. The measurements included diverse diets (normal vs. high fat/protein) in normal energy balance and pre-/post interventions of exercise, fasting, and a high-fat meal. It can be concluded that the indirect calorimetry portable method in conjunction with urine urea methods are important to help the understanding of substrate utilization in human subjects, and therefore, excellent tools to contribute to the treatments and interventions of obesity and overweighted populations.
ContributorsPradhan, Ayushi (Author) / Forzani, Erica (Thesis advisor) / Lind, Mary Laura (Committee member) / Wang, Shaopeng (Committee member) / Arizona State University (Publisher)
Created2023
Description
Bimanual robot manipulation, involving the coordinated control of two robot arms, holds great promise for enhancing the dexterity and efficiency of robotic systems across a wide range of applications, from manufacturing and healthcare to household chores and logistics. However, enabling robots to perform complex bimanual tasks with the same level of skill and adaptability as humans remains a challenging problem. The control of a bimanual robot can be tackled through various methods like inverse dynamic controller or reinforcement learning, but each of these methods have their own problems. Inverse dynamic controller cannot adapt to a changing environment, whereas Reinforcement learning is computationally intensive and may require weeks of training for even simple tasks, and reward formulation for Reinforcement Learning is often challenging and is still an open research topic. Imitation learning, leverages human demonstrations to enable robots to acquire the skills necessary for complex tasks and it can be highly sample-efficient and reduces exploration. Given the advantages of Imitation learning we want to explore the application of imitation learning techniques to bridge the gap between human expertise and robotic dexterity in the context of bimanual manipulation. In this thesis, an examination of the Implicit Behavioral Cloning imitation learning algorithm is conducted. Implicit behavioral cloning aims to capture the fundamental behavior or policy of the expert by utilizing energy-based models, which frequently demonstrate superior performance when compared to explicit behavior cloning policies. The assessment encompasses an investigation of the impact of expert demonstrations' quality on the efficacy of the acquired policies. Furthermore, computational and performance metrics of diverse training and inference techniques for energy-based models are compared.
ContributorsRayavarapu, Ravi Swaroop (Author) / Amor, Heni Ben (Thesis advisor) / Gopalan, Nakul (Committee member) / Senanayake, Ransalu (Committee member) / Arizona State University (Publisher)
Created2023
Description
The world faces significant environmental and social challenges due to high economic development, population growth, industrialization, rapid urbanization, and unsustainable consumption. Global communities are taking the necessary measures to confront these international challenges and applying sustainable development principles across all sectors. Construction is a critical driving instrument of economic activity, and to achieve sustainable development, it is vital to transform conventional construction into a more sustainable model. The research investigated sustainable construction perceptions in Kuwait, a rapidly growing country with a high volume of construction activities. Kuwait has ambitious plans to transition into a more sustainable economic development model, and the construction industry needs to align with these plans. This research aims to identify the characteristics of sustainable construction applications in the Kuwaiti construction market, such as awareness, current perceptions, drivers and barriers, and the construction regulations' impact. The research utilized a qualitative approach to answer research questions and deliver research objectives by conducting eleven Semi-structured interviews with experienced professionals in the Kuwaiti construction market to collect rich data that reflects insights and understandings of the Kuwaiti construction industry. The Thematic analysis of the data resulted in six themes and one sub-theme that presented reflections, insights, and perspectives on sustainable construction perceptions in the Kuwaiti construction market. The research findings reflected poor sustainable construction awareness and poor environmental and social application in the construction industry, the determinant role of construction regulations in promoting sustainable construction. and barriers and drivers to sustainable construction applications. The research concluded with answers to research questions, delivery of research objectives, and an explanation of sustainable construction perceptions in the Kuwaiti construction market.
Contributorsalsalem, mohammad salem (Author) / Duran, Melanie (Thesis advisor) / Chong, Oswald (Committee member) / Sullivan, Kenneth (Committee member) / Grau, David (Committee member) / Arizona State University (Publisher)
Created2023
Description
Delamination of solar module interfaces often occurs in field-tested solar modules after decades of service due to environmental stressors such as humidity. In the presence of water, the interfaces between the encapsulant and the cell, glass, and backsheet all experience losses of adhesion, exposing the module to accelerated degradation. Understanding the relation between interfacial adhesion and water content inside photovoltaic modules can help mitigate detrimental power losses. Water content measurements via water reflectometry detection combined with 180° peel tests were used to study adhesion of module materials exposed to damp heat and dry heat conditions. The effect of temperature, cumulative water dose, and water content on interfacial adhesion between ethylene vinyl acetate and (1) glass, (2) front of the cell, and (3) backsheet was studied. Temperature and time decreased adhesion at all these interfaces. Water content in the sample during the measurement showed significant decreases in adhesion for the Backsheet/Ethylene vinyl acetate interface. Water dose showed little effect for the Glass/ Ethylene vinyl acetate and Backsheet/ Ethylene vinyl acetate interfaces, but there was significant adhesion loss with water dose at the front cell busbar/encapsulant interface. Initial tensile test results to monitor the effects of the mechanical properties ethylene vinyl acetate and backsheet showed water content increasing the strength of ethylene vinyl acetate during plastic deformation but no change in the strength of the backsheet properties. This mechanical property change is likely inducing variation along the peel interface to possibly convolute the adhesion measurements conducted or to explain the variation seen for the water saturated and dried peel test sample types.
ContributorsTheut, Nicholas (Author) / Bertoni, Mariana (Thesis advisor) / Holman, Zachary (Committee member) / Chan, Candace (Committee member) / Arizona State University (Publisher)
Created2020
Description
Multi-agent reinforcement learning (MARL) plays a pivotal role in artificial intelligence by facilitating the learning process in complex environments inhabited by multiple entities. This thesis explores the integration of learning high-level knowledge through reward machines (RMs) with MARL to effectively manage non-Markovian reward functions in non-cooperative stochastic games. Reward machines offer a sophisticated way to model the temporal structure of rewards, thereby providing an enhanced representation of agent decision-making processes. A novel algorithm JIRP-SG is introduced, enabling agents to concurrently learn RMs and optimize their best response policies while navigating the intricate temporal dependencies present in non-cooperative settings. This approach employs automata learning to iteratively acquire RMs and utilizes the Lemke-Howson method to update the Q-functions, aiming for a Nash equilibrium. It is demonstrated that the method introduced reliably converges to accurately encode the reward functions and achieve the optimal best response policy for each agent over time. The effectiveness of the proposed approach is validated through case studies, including a Pacman Game scenario and a Factory Assembly scenario, illustrating its superior performance compared to baseline methods. Additionally, the impact of batch size on learning performance is examined, revealing that a diligent agent employing smaller batches can surpass the performance of an agent using larger batches, which fails to summarize experiences as effectively.
ContributorsKim, Hyohun (Author) / Xu, Zhe ZX (Thesis advisor) / Lee, Hyunglae HL (Committee member) / Berman, Spring SB (Committee member) / Arizona State University (Publisher)
Created2024