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- All Subjects: robotics
- All Subjects: chemical engineering
- Creators: Berman, Spring
- Creators: Emady, Heather
- Member of: ASU Electronic Theses and Dissertations
- Resource Type: Text
Description
Humans have an inherent capability of performing highly dexterous and skillful tasks with their arms, involving maintaining posture, movement and interacting with the environment. The latter requires for them to control the dynamic characteristics of the upper limb musculoskeletal system. Inertia, damping and stiffness, a measure of mechanical impedance, gives a strong representation of these characteristics. Many previous studies have shown that the arm posture is a dominant factor for determining the end point impedance in a horizontal plane (transverse plane). The objective of this thesis is to characterize end point impedance of the human arm in the three dimensional (3D) space. Moreover, it investigates and models the control of the arm impedance due to increasing levels of muscle co-contraction. The characterization is done through experimental trials where human subjects maintained arm posture, while perturbed by a robot arm. Moreover, the subjects were asked to control the level of their arm muscles' co-contraction, using visual feedback of their muscles' activation, in order to investigate the effect of the muscle co-contraction on the arm impedance. The results of this study showed a very interesting, anisotropic increase of the arm stiffness due to muscle co-contraction. This can lead to very useful conclusions about the arm biomechanics as well as many implications for human motor control and more specifically the control of arm impedance through muscle co-contraction. The study finds implications for the EMG-based control of robots that physically interact with humans.
ContributorsPatel, Harshil Naresh (Author) / Artemiadis, Panagiotis (Thesis advisor) / Berman, Spring (Committee member) / Helms Tillery, Stephen (Committee member) / Arizona State University (Publisher)
Created2013
Description
Membrane based technology is one of the principal methods currently in widespread use to address the global water shortage. Pervaporation desalination is a membrane technology for water purification currently under investigation as a method for processing reverse osmosis concentrates or for stand-alone applications. Concentration polarization is a potential problem in any membrane separation. In desalination concentration polarization can lead to reduced water flux, increased propensity for membrane scaling, and decreased quality of the product water. Quantifying concentration polarization is important because reducing concentration polarization requires increased capital and operating costs in the form of feed spacers and high feed flow velocities. The prevalent methods for quantifying concentration polarization are based on the steady state thin film boundary layer theory. Baker’s method, previously used for pervaporation volatile organic compound separations but not desalination, was successfully applied to data from five previously published pervaporation desalination studies. Further investigation suggests that Baker’s method may not have wide applicability in desalination. Instead, the limitations of the steady state assumption were exposed. Additionally, preliminary results of nanophotonic enhancement of pervaporation membranes were found to produce significant flux enhancement. A novel theory on the mitigation of concentration polarization by the photothermal effect was discussed.
ContributorsMann, Stewart, Ph.D (Author) / Lind, Mary Laura (Thesis advisor) / Walker, Shane (Committee member) / Green, Matthew (Committee member) / Forzani, Erica (Committee member) / Emady, Heather (Committee member) / Arizona State University (Publisher)
Created2019
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
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
Electrolytes play a critical role in electrochemical devices and applications, and therefore design and development of electrolytes with tailored properties are much desired to accommodate variety of operation requirements. Extreme temperatures are considered as one of the challenging environmental conditions, especially for devices rely on liquid state electrolytes, rendering failure of operations once the electrolyte systems undergo phase transitions. This work focuses on development of low-temperature iodide-containing liquid electrolyte systems, specifically designed for the molecular electronic transducer (MET) sensors in space applications. Utilizing ionic liquids, molecular liquids, and salts, multiple low-temperature liquid electrolytes were designed with enhancements in thermal, transport, and electrochemical properties. Effects of intermolecular interactions were further investigated, revealing correlations between optimization of microscopic dynamics and improvements of macroscopic characteristics. As a result, three low-temperature electrolyte systems were reported utilizing ethylammonium/water, gamma-butyrolactone/propylene carbonate, and butyronitrile as solvent with ionic liquid, 1-butyl-3-methylimidazolium iodide, and lithium iodide salt. Consequently, the liquidus range of these systems have been extended to -108 ˚C, -120 ˚C, and -152 ˚C, respectively, marking the lowest liquidus temperature of liquid electrolytes to the author’s best knowledge. Moreover, transport properties of designed systems were characterized from 25 to -75 ˚C. Effects of selected cosolvent/solvent on evolutions of transport properties were observed, revealing interplay between two governing mechanisms, ion disassociation and ion mobility, and their dominance at different temperatures. Experimental spectroscopy characterization techniques validated the hypothesized intermolecular interactions between solvent-cation and solvent-anion, complimented by computational simulation results on the complex dynamics between constituent ions and molecules. To support MET sensing technology, the essential iodide/triiodide redox were investigated in developed electrolytes. Effects of different molecular solvents on electrochemical kinetics were elucidated, and steady performances were validated under a properly controlled electrochemical window. Optimized electrolytes were tested in the MET sensor prototypes and showcased adequate functionality from calibration. The MET sensor prototype has also successfully detected real-time earthquake with low noise floor during long term testing at ASU seismology facility. The presented work demonstrates a facile design strategy for task-specific electrolyte development, which is anticipated to be further expanded to high temperatures for broader applications in the future.
ContributorsLin, Wendy Jessica (Author) / Dai, Lenore L (Thesis advisor) / Wiegart, Yu-chen Karen (Committee member) / Emady, Heather (Committee member) / Lind Thomas, MaryLaura (Committee member) / Torres, Cesar (Committee member) / Arizona State University (Publisher)
Created2022
Description
Despite the rapid adoption of robotics and machine learning in industry, their application to scientific studies remains under-explored. Combining industry-driven advances with scientific exploration provides new perspectives and a greater understanding of the planet and its environmental processes. Focusing on rock detection, mapping, and dynamics analysis, I present technical approaches and scientific results of developing robotics and machine learning technologies for geomorphology and seismic hazard analysis. I demonstrate an interdisciplinary research direction to push the frontiers of both robotics and geosciences, with potential translational contributions to commercial applications for hazard monitoring and prospecting. To understand the effects of rocky fault scarp development on rock trait distributions, I present a data-processing pipeline that utilizes unpiloted aerial vehicles (UAVs) and deep learning to segment densely distributed rocks in several orders of magnitude. Quantification and correlation analysis of rock trait distributions demonstrate a statistical approach for geomorphology studies. Fragile geological features such as precariously balanced rocks (PBRs) provide upper-bound ground motion constraints for hazard analysis. I develop an offboard method and onboard method as complementary to each other for PBR searching and mapping. Using deep learning, the offboard method segments PBRs in point clouds reconstructed from UAV surveys. The onboard method equips a UAV with edge-computing devices and stereo cameras, enabling onboard machine learning for real-time PBR search, detection, and mapping during surveillance. The offboard method provides an efficient solution to find PBR candidates in existing point clouds, which is useful for field reconnaissance. The onboard method emphasizes mapping individual PBRs for their complete visible surface features, such as basal contacts with pedestals–critical geometry to analyze fragility. After PBRs are mapped, I investigate PBR dynamics by building a virtual shake robot (VSR) that simulates ground motions to test PBR overturning. The VSR demonstrates that ground motion directions and niches are important factors determining PBR fragility, which were rarely considered in previous studies. The VSR also enables PBR large-displacement studies by tracking a toppled-PBR trajectory, presenting novel methods of rockfall hazard zoning. I build a real mini shake robot providing a reverse method to validate simulation experiments in the VSR.
ContributorsChen, Zhiang (Author) / Arrowsmith, Ramon (Thesis advisor) / Das, Jnaneshwar (Thesis advisor) / Bell, James (Committee member) / Berman, Spring (Committee member) / Christensen, Philip (Committee member) / Whipple, Kelin (Committee member) / Arizona State University (Publisher)
Created2022
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
Lithium-ion and lithium-metal batteries represent a predominant energy storage solution with the potential to address the impending global energy crisis arising from limited non-renewable resources. However, these batteries face significant safety challenges that hinder their commercialization. The conventional polymeric separators and electrolytes have poor thermal stability and fireproof properties making them prone to thermal runaway that causes fire hazards and explosions when the battery is subjected to extreme operating conditions. To address this issue, various materials have been investigated for their use as separators. However, polymeric, and pure inorganic material-based separators have a trade-off between safety and electrochemical performance. This is where zeolites emerge as a promising solution, offering favorable thermal and electrochemical characteristics. The zeolites are coated onto the cathode as a separator using the scalable blade coating method. These separators are non-flammable with high thermal stability and electrolyte wettability. Furthermore, the presence of intracrystalline pores helps in homogenizing the Li-ion flux at anode resulting in improved electrochemical performance. This research delves into the preparation of zeolite separators using a commercial zeolite and lab-scale zeolite to study their safety and electrochemical performance in lithium-ion batteries. At low C-rates, both zeolites exhibited excellent capacity retention and capacity density displaying their potential to advance high-performance safe lithium-ion batteries. The commercial zeolite has demonstrated remarkable capacity retention and good performance in terms of charge and discharge cycles, as well as stability. This makes it a valuable resource for the scaling up of electrode-coated separator technology.
Furthermore, the previous study demonstrated the superior electrochemical performance of plate silicalite separator (also a lab-made zeolite) with both lithium-ion and lithium-metal batteries. However, the process of scaling up and achieving precise control over plate silicalite particle size, and morphology using the existing synthesis procedure has proven challenging. Thus, the modification of process conditions is studied to enhance control over particle size, aspect ratio, and yield to facilitate a more efficient scaling-up process. Incorporation of stirring during the crystallization phase enhanced yield and uniformity of particle size. Also, the increase in temperature and time of crystallization enlarged the particles but did not show any significant improvement in the aspect ratio of the particles.
ContributorsNalam, Ramasai Dharani Harika (Author) / Lin, Jerry (Thesis advisor) / Emady, Heather (Committee member) / Seo, S. Eileen (Committee member) / Arizona State University (Publisher)
Created2023
Description
Soft robotics has garnered attention for its substantial prospective in various domains, such as manipulation and interactions with humans, by offering competitive advantages against rigid robotic systems, including inherent compliance and variable stiffness. Despite these benefits, their theoretically infinite degrees of freedom and prominent nonlinearities pose significant challenges in developing dynamic models and guiding the robots along desired paths. Additionally, soft robots may exhibit rigid behaviors and potentially collide with their surroundings during path tracking tasks, particularly when possible contact points are unknown. In this dissertation, reduced-order models are used to describe the behaviors of three different soft robot designs, including both linear parameter varying (LPV) and augmented rigid robot (ARR) models. While the reduced-order model captures the majority of the soft robot's dynamics, modeling uncertainties notably remain. Non-repeated modeling uncertainties are addressed by categorizing them as a lumped disturbance, employing two methodologies, $H_\infty$ method and nonlinear disturbance observer (NDOB) based sliding mode control, for its rejection. For repeated disturbances, an iterative learning control (ILC) with a P-type learning function is implemented to enhance trajectory tracking efficacy. Furthermore,for non-repeated disturbances, the NDOB facilitates the contact estimation, and its results are jointly used with a switching algorithm to modify the robot trajectories. The stability proof of all controllers and corresponding simulation and experimental results are provided. For a path tracking task of a soft robot with multi-segments, a robust control strategy that combines a LPV model with an innovative improved nonlinear disturbance observer-based adaptive sliding mode control (INASMC). The control framework employs a first-order LPV model for dynamic representation, leverages an improved disturbance observer for accurate disturbance forecasting, and utilizes adaptive sliding mode control to effectively counteract uncertainties. The tracking error under the proposed controller is proven to be asymptotically stable, and the controller's effectiveness is is validated with simulation and experimental results. Ultimately, this research mitigates the inherent uncertainty in soft robot modeling, thereby enhancing their functionality in contact-intensive tasks.
ContributorsQIAO, ZHI (Author) / Zhang, Wenlong (Thesis advisor) / Marvi, Hamidreza (Committee member) / Lee, Hyunglae (Committee member) / Berman, Spring (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2023
Description
Electrospun fibrous membranes have gained increasing interest in membrane filtration applications due to their high surface area and porosity. To develop a high-performance water filtration membrane a novel zwitterionic functionalized zwitterionic Polysulfone was Electrospun to bead free fibers on Polysulfone membranes. The SBAES25 was successfully Electrospun on Polysulfone membrane and thermal pressed at above Tg to improve the properties of membrane. The aim of this work is to study Electrospun zwitterionic Polysulfone nanofiber membrane with different characterization methods. The electrospinning method was studied using different polymer concentrations and electrospinning conditions. Scanning Electron Microscopy was used to study the porosity and diameter size of the fiber. TGA-ASSAY method was used to study the difference in water uptake ratio of Polysulfone membrane with and without the Electrospun fiber. A goniometer was used to test the water contact angle of the membrane. Tensile tests were performed to study the improvements in mechanical properties.
ContributorsErravelly, Nitheesh Kumar (Author) / Green, Matthew (Thesis advisor) / Emady, Heather (Committee member) / Seo, Eileen S (Committee member) / Arizona State University (Publisher)
Created2023