Matching Items (13)
Description
This dissertation research has involved microscopic characterization of magnetic nanostructures using off-axis electron holography and Lorentz microscopy. The nanostructures investigated have included Co nanoparticles (NPs), Au/Fe/GaAs shell/core nanowires (NWs), carbon spirals with magnetic cores, magnetic nanopillars, Ni-Zn-Co spinel ferrite and CoFe/Pd multilayers. The studies have confirmed the capability of holography to describe the behavior of magnetic structures at the nanoscale.
The phase changes caused by the fringing fields of chains consisting of Co NPs were measured and calculated. The difference between chains with different numbers of Co NPs followed the trend indicated by calculations. Holography studies of Au/Fe/GaAs NWs grown on (110) GaAs substrates with rotationally non-uniform coating confirmed that Fe was present in the shell and that the shell behaved as a bar magnet. No fringing field was observed from NWs with cylindrical coating grown on (111)B GaAs substrates. The most likely explanation is that magnetic fields are confined within the shells and form closed loops. The multiple-magnetic-domain structure of iron carbide cores in carbon spirals was imaged using phase maps of the fringing fields. The strength and range of this fringing field was insufficient for manipulating the carbon spirals with an external applied magnetic field. No magnetism was revealed for CoPd/Fe/CoPd magnetic nanopillars. Degaussing and MFM scans ruled out the possibility that saturated magnetization and sample preparation had degraded the anisotropy, and the magnetism, respectively. The results suggested that these nanopillars were not suitable as candidates for prototypical bit information storage devices.
Observations of Ni-Zn-Co spinel ferrite thin films in plan-view geometry indicated a multigrain magnetic domain structure and the magnetic fields were oriented in-plane only with no preferred magnetization distribution. This domain structure helps explain this ferrite's high permeability at high resonance frequency, which is an unusual character.
Perpendicular magnetic anisotropy (PMA) of CoFe/Pd multilayers was revealed using holography. Detailed microscopic characterization showed structural factors such as layer waviness and interdiffusion that could contribute to degradation of the PMA. However, these factors are overwhelmed by the dominant effect of the CoFe layer thickness, and can be ignored when considering magnetic domain structure.
The phase changes caused by the fringing fields of chains consisting of Co NPs were measured and calculated. The difference between chains with different numbers of Co NPs followed the trend indicated by calculations. Holography studies of Au/Fe/GaAs NWs grown on (110) GaAs substrates with rotationally non-uniform coating confirmed that Fe was present in the shell and that the shell behaved as a bar magnet. No fringing field was observed from NWs with cylindrical coating grown on (111)B GaAs substrates. The most likely explanation is that magnetic fields are confined within the shells and form closed loops. The multiple-magnetic-domain structure of iron carbide cores in carbon spirals was imaged using phase maps of the fringing fields. The strength and range of this fringing field was insufficient for manipulating the carbon spirals with an external applied magnetic field. No magnetism was revealed for CoPd/Fe/CoPd magnetic nanopillars. Degaussing and MFM scans ruled out the possibility that saturated magnetization and sample preparation had degraded the anisotropy, and the magnetism, respectively. The results suggested that these nanopillars were not suitable as candidates for prototypical bit information storage devices.
Observations of Ni-Zn-Co spinel ferrite thin films in plan-view geometry indicated a multigrain magnetic domain structure and the magnetic fields were oriented in-plane only with no preferred magnetization distribution. This domain structure helps explain this ferrite's high permeability at high resonance frequency, which is an unusual character.
Perpendicular magnetic anisotropy (PMA) of CoFe/Pd multilayers was revealed using holography. Detailed microscopic characterization showed structural factors such as layer waviness and interdiffusion that could contribute to degradation of the PMA. However, these factors are overwhelmed by the dominant effect of the CoFe layer thickness, and can be ignored when considering magnetic domain structure.
ContributorsZhang, Desai (Author) / Mccartney, Martha R (Thesis advisor) / Smith, David J. (Thesis advisor) / Crozier, Peter A. (Committee member) / Petusky, William T (Committee member) / Chamberlin, Ralph V (Committee member) / Arizona State University (Publisher)
Created2015
Description
In this work, a new method, "Nanobonding" [1,2] is conceived and researched to bond Si-based surfaces, via nucleation and growth of a 2 D silicon oxide SiOxHx interphase connecting the surfaces at the nanoscale across macroscopic domains. Nanobonding cross-bridges two smooth surfaces put into mechanical contact in an O2/H2O mixed ambient below T <200 °C via arrays of SiOxHx molecules connecting into a continuous macroscopic bonding interphase. Nano-scale surface planarization via wet chemical processing and new spin technology are compared via Tapping Mode Atomic Force Microscopy (TMAFM) , before and after nano-bonding. Nanobonding uses precursor phases, 2D nano-films of beta-cristobalite (beta-c) SiO2, nucleated on Si(100) via the Herbots-Atluri (H-A) method [1]. beta-c SiO2 on Si(100) is ordered and flat with atomic terraces over 20 nm wide, well above 2 nm found in native oxides. When contacted with SiO2 this ultra-smooth nanophase can nucleate and grow domains with cross-bridging molecular strands of hydroxylated SiOx, instead of point contacts. The high density of molecular bonds across extended terraces forms a strong bond between Si-based substrates, nano- bonding [2] the Si and silica. A new model of beta-cristobalite SiO2 with its <110> axis aligned along Si[100] direction is simulated via ab-initio methods in a nano-bonded stack with beta-c SiO2 in contact with amorphous SiO2 (a-SiO2), modelling cross-bridging molecular bonds between beta-c SiO2 on Si(100) and a-SiO2 as during nanobonding. Computed total energies are compared with those found for Si(100) and a-SiO2 and show that the presence of two lattice cells of !-c SiO2 on Si(100) and a-SiO2 lowers energy when compared to Si(100)/ a-SiO2 Shadow cone calculations on three models of beta-c SiO2 on Si(100) are compared with Ion Beam Analysis of H-A processed Si(100). Total surface energy measurements via 3 liquid contact angle analysis of Si(100) after H-A method processing are also compared. By combining nanobonding experiments, TMAFM results, surface energy data, and ab-initio calculations, an atomistic model is derived and nanobonding is optimized. [1] US Patent 6,613,677 (9/2/03), 7,851,365 (12/14/10), [2] Patent Filed: 4/30/09, 10/1/2011
ContributorsWhaley, Shawn D (Author) / Culbertson, Robert J. (Thesis advisor) / Herbots, Nicole (Committee member) / Rez, Peter (Committee member) / Marzke, Robert F (Committee member) / Lindsay, Stuart (Committee member) / Chamberlin, Ralph V (Committee member) / Arizona State University (Publisher)
Created2011
Description
III-nitride alloys are wide band gap semiconductors with a broad range of applications in optoelectronic devices such as light emitting diodes and laser diodes. Indium gallium nitride light emitting diodes have been successfully produced over the past decade. But the progress of green emission light emitting devices has been limited by the incorporation of indium in the alloy, mainly due to phase separation. This difficulty could be addressed by studying the growth and thermodynamics of these alloys. Knowledge of thermodynamic phase stabilities and of pressure - temperature - composition phase diagrams is important for an understanding of the boundary conditions of a variety of growth techniques. In this dissertation a study of the phase separation of indium gallium nitride is conducted using a regular solution model of the ternary alloy system. Graphs of Gibbs free energy of mixing were produced for a range of temperatures. Binodal and spinodal decomposition curves show the stable and unstable regions of the alloy in equilibrium. The growth of gallium nitride and indium gallium nitride was attempted by the reaction of molten gallium - indium alloy with ammonia at atmospheric pressure. Characterization by X-ray diffraction, photoluminescence, and secondary electron microscopy show that the samples produced by this method contain only gallium nitride in the hexagonal phase. The instability of indium nitride at the temperatures required for activation of ammonia accounts for these results. The photoluminescence spectra show a correlation between the intensity of a broad green emission, related to native defects, and indium composition used in the molten alloy. A different growth method was used to grow two columnar-structured gallium nitride films using ammonium chloride and gallium as reactants and nitrogen and ammonia as carrier gasses. Investigation by X-ray diffraction and spatially-resolved cathodoluminescence shows the film grown at higher temperature to be primarily hexagonal with small quantities of cubic crystallites, while the one grown at lower temperature to be pure hexagonal. This was also confirmed by low temperature photoluminescence measurements. The results presented here show that cubic and hexagonal crystallites can coexist, with the cubic phase having a much sharper and stronger luminescence. Controlled growth of the cubic phase GaN crystallites can be of use for high efficiency light detecting and emitting devices. The ammonolysis of a precursor was used to grow InGaN powders with different indium composition. High purity hexagonal GaN and InN were obtained. XRD spectra showed complete phase separation for samples with x < 30%, with ~ 9% indium incorporation in the 30% sample. The presence of InGaN in this sample was confirmed by PL measurements, where luminescence from both GaN and InGaN band edge are observed. The growth of higher indium compositions samples proved to be difficult, with only the presence of InN in the sample. Nonetheless, by controlling parameters like temperature and time may lead to successful growth of this III-nitride alloy by this method.
ContributorsHill, Arlinda (Author) / Ponce, Fernando A. (Thesis advisor) / Chamberlin, Ralph V (Committee member) / Sankey, Otto F (Committee member) / Smith, David J. (Committee member) / Tsen, Kong-Thon (Committee member) / Arizona State University (Publisher)
Created2011
Description
Doping and alloying agents are commonly used to engineer the properties of magnetic materials. This study investigates the effects of doping manganese in thin films of Ni80Fe20 (permalloy) and Ni65Fe15Co20 magnetic systems for low power memory technologies, including those that operate at low temperature.
Elemental manganese is anti-ferromagnetic with a Neel temperature of 100 K. When used as a dopant in a magnetic material, it is found to often align its moment in an antiferromagnetic direction. Thus, the addition of manganese might be expected to reduce the overall saturation magnetization (MS) of the magnetic system. In this study, we show that the use of manganese dopants in Ni80Fe20 (permalloy) and Ni65Fe15Co20 thin films can reduce their saturation magnetization and still retain excellent switching properties.
Magnetic properties and transport properties were determined using Vibrating Sample Magnetometer. A 19% decrease in the MS of (Ni80Fe20)1-xMnx thin films and a 36% decrease for (Ni65Fe15Co20)1-xMnx thin films for dopant levels of x = 30%. The impact of depositing a ruthenium (Ru) under-layer for (Ni65Fe15Co20)1-xMnx system was also studied.
The structural (lattice parameters and phases), surface (roughness and topography) and electrical properties (resistivity and mean free path) of the Mn-doped Ni65Fe15Co20 films were determined with X-Ray Diffraction, Atomic Force Microscopy and Four-Point probe technique respectively.
The properties were analyzed and Ni65Fe15Co20 system with Ru- under-layer with 20 at. % Mn content was found to exhibit the following low-field switching properties at 10 K; MS~700 emu.cm-3, easy axis coercivity ~10 Oe and hard axis coercivity ~5 Oe, easy axis squareness ~0.9 and anisotropy field ~12 Oe, that are deemed useful for low-power memory applications that could be used at cryogenic temperatures.
To determine the transport properties thought these magnetic layers for use in superconductor/ferromagnetic memory structures, a study of the oxidation conditions of Al films was performed in order to produce a reliable aluminum oxide tunnel barrier on top of these films. The production of N-I-F-S (Normal metal-Insulator-Ferromagnet-Superconductor) tunnel junctions will allow for the investigation of the tunneling density of states as a function of ferromagnetic layer thickness, allowing for the determination of important transport parameters relevant to magnetic barrier Josephson junction devices.
Elemental manganese is anti-ferromagnetic with a Neel temperature of 100 K. When used as a dopant in a magnetic material, it is found to often align its moment in an antiferromagnetic direction. Thus, the addition of manganese might be expected to reduce the overall saturation magnetization (MS) of the magnetic system. In this study, we show that the use of manganese dopants in Ni80Fe20 (permalloy) and Ni65Fe15Co20 thin films can reduce their saturation magnetization and still retain excellent switching properties.
Magnetic properties and transport properties were determined using Vibrating Sample Magnetometer. A 19% decrease in the MS of (Ni80Fe20)1-xMnx thin films and a 36% decrease for (Ni65Fe15Co20)1-xMnx thin films for dopant levels of x = 30%. The impact of depositing a ruthenium (Ru) under-layer for (Ni65Fe15Co20)1-xMnx system was also studied.
The structural (lattice parameters and phases), surface (roughness and topography) and electrical properties (resistivity and mean free path) of the Mn-doped Ni65Fe15Co20 films were determined with X-Ray Diffraction, Atomic Force Microscopy and Four-Point probe technique respectively.
The properties were analyzed and Ni65Fe15Co20 system with Ru- under-layer with 20 at. % Mn content was found to exhibit the following low-field switching properties at 10 K; MS~700 emu.cm-3, easy axis coercivity ~10 Oe and hard axis coercivity ~5 Oe, easy axis squareness ~0.9 and anisotropy field ~12 Oe, that are deemed useful for low-power memory applications that could be used at cryogenic temperatures.
To determine the transport properties thought these magnetic layers for use in superconductor/ferromagnetic memory structures, a study of the oxidation conditions of Al films was performed in order to produce a reliable aluminum oxide tunnel barrier on top of these films. The production of N-I-F-S (Normal metal-Insulator-Ferromagnet-Superconductor) tunnel junctions will allow for the investigation of the tunneling density of states as a function of ferromagnetic layer thickness, allowing for the determination of important transport parameters relevant to magnetic barrier Josephson junction devices.
ContributorsBoochakravarthy, Ashwin Agathya (Author) / Newman, Nathan (Thesis advisor) / Alford, Terry L. (Committee member) / Singh, Rakesh K. (Committee member) / Chamberlin, Ralph V (Committee member) / Arizona State University (Publisher)
Created2018
Description
What makes living systems different than non-living ones? Unfortunately this question is impossible to answer, at least currently. Instead, we must face computationally tangible questions based on our current understanding of physics, computation, information, and biology. Yet we have few insights into how living systems might quantifiably differ from their non-living counterparts, as in a mathematical foundation to explain away our observations of biological evolution, emergence, innovation, and organization. The development of a theory of living systems, if at all possible, demands a mathematical understanding of how data generated by complex biological systems changes over time. In addition, this theory ought to be broad enough as to not be constrained to an Earth-based biochemistry. In this dissertation, the philosophy of studying living systems from the perspective of traditional physics is first explored as a motivating discussion for subsequent research. Traditionally, we have often thought of the physical world from a bottom-up approach: things happening on a smaller scale aggregate into things happening on a larger scale. In addition, the laws of physics are generally considered static over time. Research suggests that biological evolution may follow dynamic laws that (at least in part) change as a function of the state of the system. Of the three featured research projects, cellular automata (CA) are used as a model to study certain aspects of living systems in two of them. These aspects include self-reference, open-ended evolution, local physical universality, subjectivity, and information processing. Open-ended evolution and local physical universality are attributed to the vast amount of innovation observed throughout biological evolution. Biological systems may distinguish themselves in terms of information processing and storage, not outside the theory of computation. The final research project concretely explores real-world phenomenon by means of mapping dominance hierarchies in the evolution of video game strategies. Though the main question of how life differs from non-life remains unanswered, the mechanisms behind open-ended evolution and physical universality are revealed.
ContributorsAdams, Alyssa M (Author) / Walker, Sara I (Thesis advisor) / Davies, Paul CW (Committee member) / Pavlic, Theodore P (Committee member) / Chamberlin, Ralph V (Committee member) / Arizona State University (Publisher)
Created2017
Description
The origin of Life on Earth is the greatest unsolved mystery in the history of science. In spite of progress in almost every scientific endeavor, we still have no clear theory, model, or framework to understand the processes that led to the emergence of life on Earth. Understanding such a processes would provide key insights into astrobiology, planetary science, geochemistry, evolutionary biology, physics, and philosophy. To date, most research on the origin of life has focused on characterizing and synthesizing the molecular building blocks of living systems. This bottom-up approach assumes that living systems are characterized by their component parts, however many of the essential features of life are system level properties which only manifest in the collective behavior of many components. In order to make progress towards solving the origin of life new modeling techniques are needed. In this dissertation I review historical approaches to modeling the origin of life. I proceed to elaborate on new approaches to understanding biology that are derived from statistical physics and prioritize the collective properties of living systems rather than the component parts. In order to study these collective properties of living systems, I develop computational models of chemical systems. Using these computational models I characterize several system level processes which have important implications for understanding the origin of life on Earth. First, I investigate a model of molecular replicators and demonstrate the existence of a phase transition which occurs dynamically in replicating systems. I characterize the properties of the phase transition and argue that living systems can be understood as a non-equilibrium state of matter with unique dynamical properties. Then I develop a model of molecular assembly based on a ribonucleic acid (RNA) system, which has been characterized in laboratory experiments. Using this model I demonstrate how the energetic properties of hydrogen bonding dictate the population level dynamics of that RNA system. Finally I return to a model of replication in which replicators are strongly coupled to their environment. I demonstrate that this dynamic coupling results in qualitatively different evolutionary dynamics than those expected in static environments. A key difference is that when environmental coupling is included, evolutionary processes do not select a single replicating species but rather a dynamically stable community which consists of many species. Finally, I conclude with a discussion of how these computational models can inform future research on the origins of life.
ContributorsMathis, Cole (Nicholas) (Author) / Walker, Sara I (Thesis advisor) / Davies, Paul CW (Committee member) / Chamberlin, Ralph V (Committee member) / Lachmann, Michael (Committee member) / Arizona State University (Publisher)
Created2018
Description
Fluctuations with a power spectral density depending on frequency as $1/f^\alpha$ ($0<\alpha<2$) are found in a wide class of systems. The number of systems exhibiting $1/f$ noise means it has far-reaching practical implications; it also suggests a possibly universal explanation, or at least a set of shared properties. Given this diversity, there are numerous models of $1/f$ noise. In this dissertation, I summarize my research into models based on linking the characteristic times of fluctuations of a quantity to its multiplicity of states. With this condition satisfied, I show that a quantity will undergo $1/f$ fluctuations and exhibit associated properties, such as slow dynamics, divergence of time scales, and ergodicity breaking. I propose that multiplicity-dependent characteristic times come about when a system shares a constant, maximized amount of entropy with a finite bath. This may be the case when systems are imperfectly coupled to their thermal environment and the exchange of conserved quantities is mediated through their local environment. To demonstrate the effects of multiplicity-dependent characteristic times, I present numerical simulations of two models. The first consists of non-interacting spins in $0$-field coupled to an explicit finite bath. This model has the advantage of being degenerate, so that its multiplicity alone determines the dynamics. Fluctuations of the alignment of this model will be compared to voltage fluctuations across a mesoscopic metal-insulator-metal junction. The second model consists of classical, interacting Heisenberg spins with a dynamic constraint that slows fluctuations according to the multiplicity of the system's alignment. Fluctuations in one component of the alignment will be compared to the flux noise in superconducting quantum interference devices (SQUIDs). Finally, I will compare both of these models to each other and some of the most popular models of $1/f$ noise, including those based on a superposition of exponential relaxation processes and those based on power law renewal processes.
ContributorsDavis, Bryce F (Author) / Chamberlin, Ralph V (Thesis advisor) / Mauskopf, Philip (Committee member) / Wolf, George (Committee member) / Beckstein, Oliver (Committee member) / Arizona State University (Publisher)
Created2018
Description
Variation in living systems and how it cascades across organizational levels is central to biology. To understand the constraints and amplifications of variation in collective systems, I mathematically study how group-level differences emerge from individual variation in eusocial-insect colonies, which are inherently diverse and easily observable individually and collectively. Considering collective processes in three species where increasing degrees of heterogeneity are relevant, I address how individual variation scales to colony-level variation and to what degree it is adaptive. In Chapter 2, I introduce a Markov-chain decision model for stochastic individual quorum-based recruitment decisions of rock-ant workers during house hunting, and how they determine collective speed--accuracy balance. Differences in the average threshold-dependent response characteristics of workers between colonies cause collective differences in decision-making. Moreover, noisy behavior may prevent drastic collective cascading into poor nests. In Chapter 3, I develop an ordinary differential equation (ODE) model to study how cognitive diversity among honey-bee foragers influences collective attention allocation between novel and familiar resources. Results provide a mechanistic basis for changes in foraging activity and preference with group composition. Moreover, sensitivity analysis reveals that the main individual driver for foraging allocation shifts from recruitment (communication) to persistence (independent effort) as colony composition changes. This might favor specific degrees of heterogeneity that best amplify communication in wild colonies. Lastly, in Chapter 4, I consider diversity in size, age, and task for nest defense in stingless bees. To better understand how these dimensions of diversity interact to balance defensive demands with other colony needs, I study their effect on colony size and task allocation through a demographic Filippov ODE model. Along each dimension, variation is beneficial in a certain range, outside of which colony adaptation and survival are compromised. This work elucidates how variation in collective properties emerges from nonlinear interactions between varying components in eusocial insects, but it can be generalized to other biological systems with similar fundamental characteristics but less empirical tractability. Moreover, it has the potential of inspiring algorithms that capitalize on heterogeneity in engineered systems where simple components with limited information and no central control must solve complex tasks.
ContributorsNavas Zuloaga, Maria Gabriela (Author) / Kang, Yun (Thesis advisor) / Smith, Brian H (Thesis advisor) / Pavlic, Theodore P (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
In a multi-robot system, locating a team robot is an important issue. If robots
can refer to the location of team robots based on information through passive action
recognition without explicit communication, various advantages (e.g. improving security
for military purposes) can be obtained. Specifically, when team robots follow
the same motion rule based on information about adjacent robots, associations can
be found between robot actions. If the association can be analyzed, this can be a clue
to the remote robot. Using these clues, it is possible to infer remote robots which are
outside of the sensor range.
In this paper, a multi-robot system is constructed using a combination of Thymio
II robotic platforms and Raspberry pi controllers. Robots moving in chain-formation
take action using motion rules based on information obtained through passive action
recognition. To find associations between robots, a regression model is created using
Deep Neural Network (DNN) and Long Short-Term Memory (LSTM), one of state-of-art technologies.
The input data of the regression model is divided into historical data, which
are consecutive positions of the robot, and observed data, which is information about the
observed robot. Historical data is sequence data that is analyzed through the LSTM
layer. The accuracy of the regression model designed using DNN can vary depending
on the quantity and quality of the input. In this thesis, three different input situations
are assumed for comparison. First, the amount of observed data is different, second, the
type of observed data is different, and third, the history length is different. Comparative
models are constructed for each case, and prediction accuracy is compared to analyze
the effect of input data on the regression model. This exploration validates that these
methods from deep learning can reduce the communication demands in coordinated
motion of multi-robot systems
can refer to the location of team robots based on information through passive action
recognition without explicit communication, various advantages (e.g. improving security
for military purposes) can be obtained. Specifically, when team robots follow
the same motion rule based on information about adjacent robots, associations can
be found between robot actions. If the association can be analyzed, this can be a clue
to the remote robot. Using these clues, it is possible to infer remote robots which are
outside of the sensor range.
In this paper, a multi-robot system is constructed using a combination of Thymio
II robotic platforms and Raspberry pi controllers. Robots moving in chain-formation
take action using motion rules based on information obtained through passive action
recognition. To find associations between robots, a regression model is created using
Deep Neural Network (DNN) and Long Short-Term Memory (LSTM), one of state-of-art technologies.
The input data of the regression model is divided into historical data, which
are consecutive positions of the robot, and observed data, which is information about the
observed robot. Historical data is sequence data that is analyzed through the LSTM
layer. The accuracy of the regression model designed using DNN can vary depending
on the quantity and quality of the input. In this thesis, three different input situations
are assumed for comparison. First, the amount of observed data is different, second, the
type of observed data is different, and third, the history length is different. Comparative
models are constructed for each case, and prediction accuracy is compared to analyze
the effect of input data on the regression model. This exploration validates that these
methods from deep learning can reduce the communication demands in coordinated
motion of multi-robot systems
ContributorsKang, Sehyeok (Author) / Pavlic, Theodore P (Thesis advisor) / Richa, Andréa W. (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2020