Matching Items (40)
Description
Automated vehicles are becoming more prevalent in the modern world. Using platoons of automated vehicles can have numerous benefits including increasing the safety of drivers as well as streamlining roadway operations. How individual automated vehicles within a platoon react to each other is essential to creating an efficient method of travel. This paper looks at two individual vehicles forming a platoon and tracks the time headway between the two. Several speed profiles are explored for the following vehicle including a triangular and trapezoidal speed profile. It is discovered that a safety violation occurs during platoon formation where the desired time headway between the vehicles is violated. The aim of this research is to explore if this violation can be eliminated or reduced through utilization of different speed profiles.
ContributorsLarson, Kurt Gregory (Author) / Lou, Yingyan (Thesis director) / Chen, Yan (Committee member) / Civil, Environmental and Sustainable Eng Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Robots are often used in long-duration scenarios, such as on the surface of Mars,where they may need to adapt to environmental changes. Typically, robots have been built specifically for single tasks, such as moving boxes in a warehouse or surveying construction sites. However, there is a modern trend away from human hand-engineering and toward robot learning. To this end, the ideal robot is not engineered,but automatically designed for a specific task. This thesis focuses on robots which learn path-planning algorithms for specific environments. Learning is accomplished via genetic programming. Path-planners are represented as Python code, which is optimized via Pareto evolution. These planners are encouraged to explore curiously and efficiently. This research asks the questions: “How can robots exhibit life-long learning where they adapt to changing environments in a robust way?”, and “How can robots learn to be curious?”.
ContributorsSaldyt, Lucas P (Author) / Ben Amor, Heni (Thesis director) / Pavlic, Theodore (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
The purpose of this project was to design a new railroad crossing for pedestrians and bicyclists in mid-block or urban areas. In order to develop a successful design, the needs of the railroad, the end-users, and the city governments were researched and converted into measurable engineering requirements. For the railroad companies, the most important need was a crossing that presents an effective barrier to users while a train is in the area. For bicyclists and pedestrians (the end-users), the most important need was for the crossing to be both reliable and easily accessible. For the city governments, the most important need was a crossing that is inexpensive yet sturdy. The approach to this project was similar to the approach used in many engineering design processes. First is the Introduction, which provides an overview of the issue and presents the full problem statement. Next is the Research of Prior Art, which details the past solutions to railroad crossings as well as the 3 E's of railroad crossing safety. After this, the customer needs are discussed in the Needs to Requirements section and the process of converting these into measurable engineering requirements is shown. Next, various conceptual design options are shown in the Conceptual Design section and a final conceptual design is chosen based on adherence to the stated requirements. This final conceptual design is then taken into the preliminary design phase and refined until it becomes the final preliminary design. After the Final Preliminary Design Description, the Project Conclusions and Recommendations are presented. Due to time and monetary constraints, this project ends after the preliminary design stage. Despite this, the conclusion of this project is that the final design presented here will be successful if additional resources are obtained to move it forward into the detailed design phase. For now, this project has come to a halt due to UP's reluctance to allow any additional railroad crossings in the Phoenix and Tempe, Arizona areas. It is recommended that city officials and bicyclist/pedestrian action groups continue talks with UP until they agree to allow additional crossings to be built that are geared towards non-motorized users.
ContributorsJones, Mitchell Drexel (Author) / Kuby, Michael (Thesis director) / Lou, Yingyan (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2015-05
Description
Recurring incidents between pedestrians, bicycles, and vehicles at the intersection of Rural Road and Spence Avenue led to a team of students conducting their own investigation into the current conditions and analyzing a handful of alternatives. An extension of an industry-standard technique was used to build a control case which alternatives would be compared to. Four alternatives were identified, and the two that could be modeled in simulation software were both found to be technically feasible in the preliminary analysis.
ContributorsFellows, Christopher Lee (Author) / Lou, Yingyan (Thesis director) / Zhou, Xuesong (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Women have evolved in the engineering profession over the decades. However, there is still a lot more room for female presence in the industry as women currently make up about 12-15% of working engineers. Based on many studies and surveys, it is clear that female confidence in their own performance and a feeling of belonging in the industry has evolved for the better. The studies and surveys also show that women still lack a certain confidence to get their engineering degree and then to pursue a career in engineering once they receive their degree. Research shows that the main cause for this is due to the stereotype that engineering is a masculine profession. Men and women both have this mindset because it has become a societal norm that most people go along with and do not even realize it. Unfortunately, it is very hard to overcome and change a societal norm, therefore, something needs to be done in order to fix this mindset. (Crawford). Based on studies and research, there are many ways the stereotype is being combatted. Social media has become a huge component in advocating for female engineers. Men and women are helping to fight the status quo by supporting female engineers and lobbying against people who think women do not belong in the industry. Industry professionals are teaming up with schools to figure out ways to make STEM programs more exciting for all young kids, but especially girls. They are also working to provide more mentors and role models for young girls in order to cheer them on and make them more confident in their abilities when learning and applying the STEM curriculum, as studies have proven that providing young girls with mentors can really help foster more female engineers in the long run. (Crawford). With all of the positive support and promotions of female engineers in the past few years, it is evident that women can certainly progress at a much faster pace than in previous decades.
ContributorsAcosta, Jazlyn (Co-author) / Venne, Hunter (Co-author) / Ward, Kristen (Thesis director) / Lou, Yingyan (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Accurate pose initialization and pose estimation are crucial requirements in on-orbit space assembly and various other autonomous on-orbit tasks. However, pose initialization and pose estimation are much more difficult to do accurately and consistently in space. This is primarily due to not only the variable lighting conditions present in space, but also the power requirements mandated by space-flyable hardware. This thesis investigates leveraging a deep learning approach for monocular one-shot pose initialization and pose estimation. A convolutional neural network was used to estimate the 6D pose of an assembly truss object. This network was trained by utilizing synthetic imagery generated from a simulation testbed. Furthermore, techniques to quantify model uncertainty of the deep learning model were investigated and applied in the task of in-space pose estimation and pose initialization. The feasibility of this approach on low-power computational platforms was also tested. The results demonstrate that accurate pose initialization and pose estimation can be conducted using a convolutional neural network. In addition, the results show that the model uncertainty can be obtained from the network. Lastly, the use of deep learning for pose initialization and pose estimation in addition with uncertainty quantification was demonstrated to be feasible on low-power compute platforms.
ContributorsKailas, Siva Maneparambil (Author) / Ben Amor, Heni (Thesis director) / Detry, Renaud (Committee member) / Economics Program in CLAS (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
In the field of machine learning, reinforcement learning stands out for its ability to explore approaches to complex, high dimensional problems that outperform even expert humans. For robotic locomotion tasks reinforcement learning provides an approach to solving them without the need for unique controllers. In this thesis, two reinforcement learning algorithms, Deep Deterministic Policy Gradient and Group Factor Policy Search are compared based upon their performance in the bipedal walking environment provided by OpenAI gym. These algorithms are evaluated on their performance in the environment and their sample efficiency.
ContributorsMcDonald, Dax (Author) / Ben Amor, Heni (Thesis director) / Yang, Yezhou (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2018-12
Description
Lyric classification and generation are trending in topics in the machine learning community. Long Short-Term Networks (LSTMs) are effective tools for classifying and generating text. We explored their effectiveness in the generation and classification of lyrical data and proposed methods of evaluating their accuracy. We found that LSTM networks with dropout layers were effective at lyric classification. We also found that Word embedding LSTM networks were extremely effective at lyric generation.
ContributorsTallapragada, Amit (Author) / Ben Amor, Heni (Thesis director) / Caviedes, Jorge (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
The volume of scientific research is growing at an exponential rate over the past100 years. With the advent of the internet and ubiquitous access to the web, academic research search engines such as Google Scholar, Microsoft Academic, etc., have become the go-to platforms for systemic reviews and search. Although many academic search engines host lots of content, they provide minimal context about where the search terms matched. Many of these search engines also fail to provide additional tools which can help enhance a researcher’s understanding of research content outside their respective websites. An example of such a tool can be a browser extension/plugin that surfaces context-relevant information about a research article when the user reads a research article. This dissertation discusses a solution developed to bring more intrinsic characteristics of research documents such as the structure of the research document, tables in the document, the keywords associated with the document to improve search capabilities and augment the information a researcher may read. The prototype solution named Sci-Genie(https://sci-genie.com/) is a search engine over scientific articles from Computer Science ArXiv. Sci-Genie parses research papers and indexes research documents’ structure to provide context-relevant information about the matched search fragments. The same search engine also powers a browser extension to augment the information about a research article the user may be reading. The browser extension augments the user’s interface with information about tables from the cited papers, other papers by the same authors, and even the citations to and from the current article. The browser extension is further powered with access endpoints that leverage a machine learning model to filter tables comparing various entities. The dissertation further discusses these machine learning models and some baselines that help classify whether a table is comparing various entities or not. The dissertation finally concludes by discussing the current shortcomings of Sci-Genie and possible future research scope based on learnings after building Sci-Genie.
ContributorsDave, Valay (Author) / Zou, Jia (Thesis advisor) / Ben Amor, Heni (Thesis advisor) / Candan, Kasim Selcuk (Committee member) / Arizona State University (Publisher)
Created2021
Description
This dissertation explores the use of artificial intelligence and machine learningtechniques for the development of controllers for fully-powered robotic prosthetics.
The aim of the research is to enable prosthetics to predict future states and control
biomechanical properties in both linear and nonlinear fashions, with a particular focus
on ergonomics.
The research is motivated by the need to provide amputees with prosthetic devices
that not only replicate the functionality of the missing limb, but also offer a high
level of comfort and usability. Traditional prosthetic devices lack the sophistication to
adjust to a user’s movement patterns and can cause discomfort and pain over time.
The proposed solution involves the development of machine learning-based controllers
that can learn from user movements and adjust the prosthetic device’s movements
accordingly.
The research involves a combination of simulation and real-world testing to evaluate
the effectiveness of the proposed approach. The simulation involves the creation of a
model of the prosthetic device and the use of machine learning algorithms to train
controllers that predict future states and control biomechanical properties. The real-
world testing involves the use of human subjects wearing the prosthetic device to
evaluate its performance and usability.
The research focuses on two main areas: the prediction of future states and the
control of biomechanical properties. The prediction of future states involves the
development of machine learning algorithms that can analyze a user’s movements
and predict the next movements with a high degree of accuracy. The control of
biomechanical properties involves the development of algorithms that can adjust the
prosthetic device’s movements to ensure maximum comfort and usability for the user.
The results of the research show that the use of artificial intelligence and machine
learning techniques can significantly improve the performance and usability of pros-
thetic devices. The machine learning-based controllers developed in this research are
capable of predicting future states and adjusting the prosthetic device’s movements in
real-time, leading to a significant improvement in ergonomics and usability. Overall,
this dissertation provides a comprehensive analysis of the use of artificial intelligence
and machine learning techniques for the development of controllers for fully-powered
robotic prosthetics.
ContributorsCLARK, GEOFFEY M (Author) / Ben Amor, Heni (Thesis advisor) / Dasarathy, Gautam (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Ward, Jeffrey (Committee member) / Arizona State University (Publisher)
Created2023