Matching Items (207)
Description
The aim of this project was to develop user-friendly methods for programming and controlling a new type of small robot platform, called Pheeno, both individually and as part of a group. Two literature reviews are presented to justify the need for these robots and to discuss what other platforms have been developed for similar applications. In order to accomplish control of multiple robots work was done on controlling a single robot first. The response of a gripper arm attachment for the robot was smoothed, graphical user interfaces were developed, and commands were sent to a single robot using a video game controller. For command of multiple robots a class was developed in Python to make it simpler to send commands and keep track of different characteristics of each individual robot. A simple script was also created as a proof of concept to show how threading could be used to send different commands simultaneously to multiple robots in order to test algorithms on a group of robots. The class and two other scripts necessary for implementing the class are also presented to make it possible for future use of the given work.
ContributorsHutchins, Gregory Scott (Author) / Berman, Spring (Thesis director) / Artemiadis, Panagiotis (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Concrete stands at the forefront of the construction industry as one of the most useful building materials. Economic and efficient improvements in concrete strengthening and manufacturing are widely sought to continuously improve the performance of the material. Fiber reinforcement is a significant technique in strengthening precast concrete, but manufacturing limitations are common which has led to reliance on steel reinforcement. Two-dimensional textile reinforcement has emerged as a strong and efficient alternative to both fiber and steel reinforced concrete with pultrusion manufacturing shown as one of the most effective methods of precasting concrete. The intention of this thesis project is to detail the components, functions, and outcomes shown in the development of an automated pultrusion system for manufacturing textile reinforced concrete (TRC). Using a preexisting, manual pultrusion system and current-day manufacturing techniques as a basis, the automated pultrusion system was designed as a series of five stations that centered on textile impregnation, system driving, and final pressing. The system was then constructed in the Arizona State University Structures Lab over the course of the spring and summer of 2015. After fabricating each station, a computer VI was coded in LabVIEW software to automatically drive the system. Upon completing construction of the system, plate and angled structural sections were then manufactured to verify the adequacy of the technique. Pultruded TRC plates were tested in tension and flexure while full-scale structural sections were tested in tension and compression. Ultimately, the automated pultrusion system was successful in establishing an efficient and consistent manufacturing process for continuous TRC sections.
ContributorsBauchmoyer, Jacob Macgregor (Author) / Mobasher, Barzin (Thesis director) / Neithalath, Narayanan (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / The Design School (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Manufacture of building materials requires significant energy, and as demand for these materials continues to increase, the energy requirement will as well. Offsetting this energy use will require increased focus on sustainable building materials. Further, the energy used in building, particularly in heating and air conditioning, accounts for 40 percent of a buildings energy use. Increasing the efficiency of building materials will reduce energy usage over the life time of the building. Current methods for maintaining the interior environment can be highly inefficient depending on the building materials selected. Materials such as concrete have low thermal efficiency and have a low heat capacity meaning it provides little insulation. Use of phase change materials (PCM) provides the opportunity to increase environmental efficiency of buildings by using the inherent latent heat storage as well as the increased heat capacity. Incorporating PCM into concrete via lightweight aggregates (LWA) by direct addition is seen as a viable option for increasing the thermal storage capabilities of concrete, thereby increasing building energy efficiency. As PCM change phase from solid to liquid, heat is absorbed from the surroundings, decreasing the demand on the air conditioning systems on a hot day or vice versa on a cold day. Further these materials provide an additional insulating capacity above the value of plain concrete. When the temperature drops outside the PCM turns back into a solid and releases the energy stored from the day. PCM is a hydrophobic material and causes reductions in compressive strength when incorporated directly into concrete, as shown in previous studies. A proposed method for mitigating this detrimental effect, while still incorporating PCM into concrete is to encapsulate the PCM in aggregate. This technique would, in theory, allow for the use of phase change materials directly in concrete, increasing the thermal efficiency of buildings, while negating the negative effect on compressive strength of the material.
ContributorsSharma, Breeann (Author) / Neithalath, Narayanan (Thesis advisor) / Mobasher, Barzin (Committee member) / Rajan, Subramaniam D. (Committee member) / Arizona State University (Publisher)
Created2013
Description
With the substantial development of intelligent robots, human-robot interaction (HRI) has become ubiquitous in applications such as collaborative manufacturing, surgical robotic operations, and autonomous driving. In all these applications, a human behavior model, which can provide predictions of human actions, is a helpful reference that helps robots to achieve intelligent interaction with humans. The requirement elicits an essential problem of how to properly model human behavior, especially when individuals are interacting or cooperating with each other. The major objective of this thesis is to utilize the human intention decoding method to help robots enhance their performance while interacting with humans. Preliminary work on integrating human intention estimation with an HRI scenario is shown to demonstrate the benefit. In order to achieve this goal, the research topic is divided into three phases. First, a novel method of an online measure of the human's reliance on the robot, which can be estimated through the intention decoding process from human actions，is described. An experiment that requires human participants to complete an object-moving task with a robot manipulator was conducted under different conditions of distractions. A relationship is discovered between human intention and trust while participants performed a familiar task with no distraction. This finding suggests a relationship between the psychological construct of trust and joint physical coordination, which bridges the human's action to its mental states. Then, a novel human collaborative dynamic model is introduced based on game theory and bounded rationality, which is a novel method to describe human dyadic behavior with the aforementioned theories. The mutual intention decoding process was also considered to inform this model. Through this model, the connection between the mental states of the individuals to their cooperative actions is indicated. A haptic interface is developed with a virtual environment and the experiments are conducted with 30 human subjects. The result suggests the existence of mutual intention decoding during the human dyadic cooperative behaviors. Last, the empirical results show that allowing agents to have empathy in inference, which lets the agents understand that others might have a false understanding of their intentions, can help to achieve correct intention inference. It has been verified that knowledge about vehicle dynamics was also important to correctly infer intentions. A new courteous policy is proposed that bounded the courteous motion using its inferred set of equilibrium motions. A simulation, which is set to reproduce an intersection passing case between an autonomous car and a human driving car, is conducted to demonstrate the benefit of the novel courteous control policy.
ContributorsWang, Yiwei (Author) / Zhang, Wenlong (Thesis advisor) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Ren, Yi (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021
Description
Building and optimizing a design for deformable media can be extremely costly. However, granular scaling laws enable the ability to predict system velocity and mobility
power consumption by testing at a smaller scale in the same environment. The validity of
the granular scaling laws for arbitrarily shaped wheels and screws were evaluated in
materials like silica sand and BP-1, a lunar simulant. Different wheel geometries, such as
non-grousered and straight and bihelically grousered wheels were created and tested
using 3D printed technologies. Using the granular scaling laws and the empirical data
from initial experiments, power and velocity were predicted for a larger scaled version
then experimentally validated on a dynamic mobility platform. Working with granular
media has high variability in material properties depending on initial environmental
conditions, so particular emphasis was placed on consistency in the testing methodology.
Through experiments, these scaling laws have been validated with defined use cases and
limitations.
ContributorsMcbryan, Teresa (Author) / Marvi, Hamidreza (Thesis advisor) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Arizona State University (Publisher)
Created2022
Description
Chemical Reaction Networks (CRNs) provide a useful framework for modeling andcontrolling large numbers of agents that undergo stochastic transitions between a set of states
in a manner similar to chemical compounds. By utilizing CRN models to design agent control
policies, some of the computational challenges in the coordination of multi-agent systems can
be overcome. In this thesis, a CRN model is developed that defines agent control policies for a
multi-agent construction task. The use of surface CRNs to overcome the tradeoff between
speed and accuracy of task performance is explained. The computational difficulties involved
in coordinating multiple agents to complete collective construction tasks is then discussed. A
method for stochastic task and motion planning (TAMP) is proposed to explain how a TAMP
solver can be applied with CRNs to coordinate multiple agents.
This work defines a collective construction scenario in which a group of noncommunicating agents must rearrange blocks on a discrete domain with obstacles into a
predefined target distribution. Four different construction tasks are considered with 10, 20,
30, or 40 blocks, and a simulation of each scenario with 2, 4, 6, or 8 agents is performed. As the
number of blocks increases, the construction problem becomes more complex, and a given
population of agents requires more time to complete the task. Populations of fewer than 8
agents are unable to solve the 30-block and 40-block problems in the allotted simulation time,
suggesting an inflection point for computational feasibility, implying that beyond that point
the solution times for fewer than 8 agents would be expected to increase significantly. For a
group of 8 agents, the time to complete the task generally increases as the number of blocks
increases, except for the 30-block problem, which has specifications that make the task slightly
easier for the agents to complete compared to the 20-block problem. For the 10-block and 20-
block problems, the time to complete the task decreases as the number of agents increases;
however, the marginal effect of each additional two agents on this time decreases. This can be
explained through the pigeonhole principle: since there area finite number of states, when the
number of agents is greater than the number of available spaces, deadlocks start to occur and
the expectation is that the overall solution time to tend to infinity.
ContributorsKamojjhala, Pranav (Author) / Berman, Spring (Thesis advisor) / Fainekos, Gergios E (Thesis advisor) / Pavlic, Theodore P (Committee member) / Arizona State University (Publisher)
Created2022
Description
A Graph Neural Network (GNN) is a type of neural network architecture that operates on data consisting of objects and their relationships, which are represented by a graph. Within the graph, nodes represent objects and edges represent associations between those objects. The representation of relationships and correlations between data is unique to graph structures. GNNs exploit this feature of graphs by augmenting both forms of data, individual and relational, and have been designed to allow for communication and sharing of data within each neural network layer. These benefits allow each node to have an enriched perspective, or a better understanding, of its neighbouring nodes and its connections to those nodes. The ability of GNNs to efficiently process high-dimensional node data and multi-faceted relationships among nodes gives them advantages over neural network architectures such as Convolutional Neural Networks (CNNs) that do not implicitly handle relational data. These quintessential characteristics of GNN models make them suitable for solving problems in which the correspondences among input data are needed to produce an accurate and precise representation of these data. GNN frameworks may significantly improve existing communication and control techniques for multi-agent tasks by implicitly representing not only information associated with the individual agents, such as agent position, velocity, and camera data, but also their relationships with one another, such as distances between the agents and their ability to communicate with one another. One such task is a multi-agent navigation problem in which the agents must coordinate with one another in a decentralized manner, using proximity sensors only, to navigate safely to their intended goal positions in the environment without collisions or deadlocks. The contribution of this thesis is the design of an end-to-end decentralized control scheme for multi-agent navigation that utilizes GNNs to prevent inter-agent collisions and deadlocks. The contributions consist of the development, simulation and evaluation of the performance of an advantage actor-critic (A2C) reinforcement learning algorithm that employs actor and critic networks for training that simultaneously approximate the policy function and value function, respectively. These networks are implemented using GNN frameworks for navigation by groups of 3, 5, 10 and 15 agents in simulated two-dimensional environments. It is observed that in $40\%$ to $50\%$ of the simulation trials, between 70$\%$ to 80$\%$ of the agents reach their goal positions without colliding with other agents or becoming trapped in deadlocks. The model is also compared to a random run simulation, where actions are chosen randomly for the agents and observe that the model performs notably well for smaller groups of agents.
ContributorsAyalasomayajula, Manaswini (Author) / Berman, Spring (Thesis advisor) / Mian, Sami (Committee member) / Pavlic, Theodore (Committee member) / Arizona State University (Publisher)
Created2022
Description
Recently, Generative Adversarial Networks (GANs) have been applied to the problem of Cold-Start Recommendation, but the training performance of these models is hampered by the extreme sparsity in warm user purchase behavior. This thesis introduces a novel representation for user-vectors by combining user demographics and user preferences, making the model a hybrid system which uses Collaborative Filtering and Content Based Recommendation. This system models user purchase behavior using weighted user-product preferences (explicit feedback) rather than binary user-product interactions (implicit feedback). Using this a novel sparse adversarial model, Sparse ReguLarized Generative Adversarial Network (SRLGAN), is developed for Cold-Start Recommendation. SRLGAN leverages the sparse user-purchase behavior which ensures training stability and avoids over-fitting on warm users. The performance of SRLGAN is evaluated on two popular datasets and demonstrate state-of-the-art results.
ContributorsShah, Aksheshkumar Ajaykumar (Author) / Venkateswara, Hemanth (Thesis advisor) / Berman, Spring (Thesis advisor) / Ladani, Leila J (Committee member) / Arizona State University (Publisher)
Created2022
Description
Soft continuum robots with the ability to bend, twist, elongate, and shorten, similar to octopus arms, have many potential applications, such as dexterous manipulation and navigation through unstructured, dynamic environments. Novel soft materials such as smart hydrogels, which change volume and other properties in response to stimuli such as temperature, pH, and chemicals, can potentially be used to construct soft robots that achieve self-regulated adaptive reconfiguration through on-demand dynamic control of local properties. However, the design of controllers for soft continuum robots is challenging due to their high-dimensional configuration space and the complexity of modeling soft actuator dynamics. To address these challenges, this dissertation presents two different model-based control approaches for robots with distributed soft actuators and sensors and validates the approaches in simulations and physical experiments. It is demonstrated that by choosing an appropriate dynamical model and designing a decentralized controller based on this model, such robots can be controlled to achieve diverse types of complex configurations. The first approach consists of approximating the dynamics of the system, including its actuators, as a linear state-space model in order to apply optimal robust control techniques such as H∞ state-feedback and H∞ output-feedback methods. These techniques are designed to utilize the decentralized control structure of the robot and its distributed sensing and actuation to achieve vibration control and trajectory tracking. The approach is validated in simulation on an Euler-Bernoulli dynamic model of a hydrogel based cantilevered robotic arm and in experiments with a hydrogel-actuated miniature 2-DOF manipulator. The second approach is developed for soft continuum robots with dynamics that can be modeled using Cosserat rod theory. An inverse dynamics control approach is implemented on the Cosserat model of the robot for tracking configurations that include bending, torsion, shear, and extension deformations. The decentralized controller structure facilitates its implementation on robot arms composed of independently-controllable segments that have local sensing and actuation. This approach is validated on simulated 3D robot arms and on an actual silicone robot arm with distributed pneumatic actuation, for which the inverse dynamics problem is solved in simulation and the computed control outputs are applied to the robot in real-time.
ContributorsDoroudchi, Azadeh (Author) / Berman, Spring (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Si, Jennie (Committee member) / Marvi, Hamid (Committee member) / Arizona State University (Publisher)
Created2022
Description政府引导基金自诞生至今，始终处于管理模式的摸索状态。本文试图从公司治理的角度分析不同利益方（政府、社会出资人以及管理人）之间的博弈关系以及其对引导基金投资效果的影响。政府引导基金的注册数量和规模在过去十几年中得到了快速显著的发展。从引导基金设立的政府行政层级来看，地县级政府设立基金是引导基金出资中的绝对主力。本文拟深入研究地县级政府引导基金的运作模式，尝试探索其治理结构与投资效果。
目前，引导基金的运作模式仍然处于摸索阶段，论文试图对引导基金若干个指标做出客观比较，分析政府参与度对资金投资效果的影响，希望对未来引导基金的设立模式选择提供有力的理论基础。为实现较好的研究效果，论文选择了某经济发达的地级市的样本进行了研究，该市的政府引导私募股权基金发展程度相对较高，市本级以及区县级均有较多的政府引导私募股权基金，该市范围政府引导私募股权基金可研究价值相对较高。在样本选择方面，论文将采样某市及所辖区县政府直接出资基金十六只，针对其参与设立的直投基金以及直接投资项目进行分析。同时，论文还总正反两个方面选择了两个经典案例进行详细剖析。
论文发现，市场化运作程度越低，引导基金所期望实现的目标效果相对不理想，投资效果越差。政府在决策中所占比重越高，形成的投资决策对于项目成长性判断的准确度越差，对地方经济社会发展的综合贡献越低。然而，纯粹的商业运作，无法实现引导基金所承担的社会使命。对不同的资金诉求导致的投资要求在不同的决策层级实现，通过政府或其代表出资方对管理方以协议约束的方式保证其投资行为而不再进行对单个项目进行价值判断，是有效实现引导诉求和专业判断兼顾的引导基金管理模式。
论文建议，在经济相对发达地区，政府引导基金应该积极采用市场化运作模式，在现有可选择的模式中，政府引导基金以LP身份且获取咨询委角色，从外部监察约束角度对基金进行投资引导的模式为最佳选择。
ContributorsWu, Di (Author) / Pei, Ker-Wei (Thesis advisor) / Wu, Fei (Thesis advisor) / Zhu, David (Committee member) / Arizona State University (Publisher)
Created2022