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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- Creators: Si, Jennie
Description
Object sorting is a very common application especially in the industry setting, but this is a labor intensive and time consuming process and it proves to be challenging if done manually. Thanks to the rapid development in technology now almost all these object sorting tasks are partially or completely automated. Image processing techniques are essential for the full operation of such a pick and place robot as it is responsible for perceiving the environment and to correctly identify ,classify and localize the different objects in it. In order for the robots to perform accurate object sorting with efficiency and stability this thesis discusses how different Deep learning based perception techniques can be used. In the era of Artificial Intelligence this sorting problem can be done more efficiently than the existing techniques. This thesis presents different image processing techniques and algorithms that can be used to perform object sorting efficiently. A comparison between three different deep learning based techniques is presented and their pros and cons are discussed. Furthermore this thesis also presents a comprehensive study about the kinematics and the dynamics involved in a 2 Degree of Freedom Robotic Manipulator .
ContributorsRanganathan, Pavithra (Author) / Rodriguez, Armando (Thesis advisor) / Si, Jennie (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2021
Description
Over the past few decades, there is an increase in demand for various ground robot applications such as warehouse management, surveillance, mapping, infrastructure inspection, etc. This steady increase in demand has led to a significant rise in the nonholonomic differential drive vehicles (DDV) research. Albeit extensive work has been done in developing various control laws for trajectory tracking, point stabilization, formation control, etc., there are still problems and critical questions in regards to design, modeling, and control of DDV’s - that need to be adequately addressed. In this thesis, three different dynamical models are considered that are formed by varying the input/output parameters of the DDV model. These models are analyzed to understand their stability, bandwidth, input-output coupling, and control design properties. Furthermore, a systematic approach has been presented to show the impact of design parameters such as mass, inertia, radius of the wheels, and center of gravity location on the dynamic and inner-loop (speed) control design properties. Subsequently, extensive simulation and hardware trade studies have been conductedto quantify the impact of design parameters and modeling variations on the performance of outer-loop cruise and position control (along a curve). In addition to this, detailed guidelines are provided for when a multi-input multi-output (MIMO) control strategy is advisable over a single-input single-output (SISO) control strategy; when a less stable plant is preferable over a more stable one in order to accommodate performance specifications. Additionally, a multi-robot trajectory tracking implementation based on receding horizon optimization approach is also presented. In most of the optimization-based trajectory tracking approaches found in the literature, only the constraints imposed by the kinematic model are incorporated into the problem. This thesis elaborates the fundamental problem associated with these methods and presents a systematic approach to understand and quantify when kinematic model based constraints are sufficient and when dynamic model-based constraints are necessary to obtain good tracking properties. Detailed instructions are given for designing and building the DDV based on performance specifications, and also, an open-source platform capable of handling high-speed multi-robot research is developed in C++.
ContributorsManne, Sai Sravan (Author) / Rodriguez, Armando A (Thesis advisor) / Si, Jennie (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2021
Description
Human-robot interactions can often be formulated as general-sum differential games where the equilibrial policies are governed by Hamilton-Jacobi-Isaacs (HJI) equations. Solving HJI PDEs faces the curse of dimensionality (CoD). While physics-informed neural networks (PINNs) alleviate CoD in solving PDEs with smooth solutions, they fall short in learning discontinuous solutions due to their sampling nature. This causes PINNs to have poor safety performance when they are applied to approximate values that are discontinuous due to state constraints. This dissertation aims to improve the safety performance of PINN-based value and policy models. The first contribution of the dissertation is to develop learning methods to approximate discontinuous values. Specifically, three solutions are developed: (1) hybrid learning uses both supervisory and PDE losses, (2) value-hardening solves HJIs with increasing Lipschitz constant on the constraint violation penalty, and (3) the epigraphical technique lifts the value to a higher-dimensional state space where it becomes continuous. Evaluations through 5D and 9D vehicle and 13D drone simulations reveal that the hybrid method outperforms others in terms of generalization and safety performance. The second contribution is a learning-theoretical analysis of PINN for value and policy approximation. Specifically, by extending the neural tangent kernel (NTK) framework, this dissertation explores why the choice of activation function significantly affects the PINN generalization performance, and why the inclusion of supervisory costate data improves the safety performance. The last contribution is a series of extensions of the hybrid PINN method to address real-time parameter estimation problems in incomplete-information games. Specifically, a Pontryagin-mode PINN is developed to avoid costly computation for supervisory data. The key idea is the introduction of a costate loss, which is cheap to compute yet effectively enables the learning of important value changes and policies in space-time. Building upon this, a Pontryagin-mode neural operator is developed to achieve state-of-the-art (SOTA) safety performance across a set of differential games with parametric state constraints. This dissertation demonstrates the utility of the resultant neural operator in estimating player constraint parameters during incomplete-information games.
ContributorsZhang, Lei (Author) / Ren, Yi (Thesis advisor) / Si, Jennie (Committee member) / Berman, Spring (Committee member) / Zhang, Wenlong (Committee member) / Xu, Zhe (Committee member) / Arizona State University (Publisher)
Created2024