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- All Subjects: engineering
- All Subjects: Robots
- Creators: Marvi, Hamidreza
Description
Traditional wheeled robots struggle to traverse granular media such as sand or mud which has inspired the use of continuous tracks, legged, and various bio-inspired designs in recent robotics research. Animals can navigate the natural world with relative ease and one animal, the Basilisk lizard, can perform the amazing feat of bipedal water and land running. Through the observation and study of basilisk lizards of the common and plumed variety, inspiration and development of a robotic platform was completed. After fabricating the bio-inspired robot, parameters unchanged by the animals were varied to characterize the combined effects of stride length and frequency on average velocity. It was found that animals increased stride length at higher saturation levels of sand to increase their velocity rather than increase their step frequency. The BasiliskBot version one was unable to change its stride length as the wheel-legs or "whegs" of this version were set at four spokes. Bipedal running of the robot was slower than quadrupedal running due to sand reaction forces and tail drag. BasiliskBot version two was lighter than the first version and had a range of stride lengths tested with increasing spoke numbers from 3-7. At lower step frequencies and lower wheg numbers, higher average velocity could be achieved compared to higher wheg numbers despite the highest maximum velocity being achieved by the highest number of spokes. A comparison of transition strategies for common and plumed basilisks showed both species chose to jump and swim through water more often than jump and run across water which achieved the highest average velocity. Results of transition strategies study pertain to future developments of the robot for amphibious purposes. Weight experiments were performed to assess the ability of the robot to carry sensors and other payloads. Added weight increased the highest frequency allowable before failure, but also caused failure at low step frequencies that had not displayed failure previously.
ContributorsBurch, Hailey (Author) / Marvi, Hamidreza (Thesis director) / Bagheri, Hosain (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Millions of individuals suffer from gait impairments due to stroke or other neurological disorders. A primary goal of patients is to walk independently, but most patients only achieve a poor functional outcome five years after injury. Despite the growing interest in using robotic devices for rehabilitation of sensorimotor function, state-of-the-art robotic interventions in gait therapy have not resulted in improved outcomes when compared to traditional treadmill-based therapy. Because bipedal walking requires neural coupling and dynamic interactions between the legs, a fundamental understanding of the sensorimotor mechanisms of inter-leg coordination during walking is needed to inform robotic interventions in gait therapy. This dissertation presents a systematic exploration of sensorimotor mechanisms of inter-leg coordination by studying the effect of unilateral perturbations of the walking surface stiffness on contralateral muscle activation in healthy populations. An analysis of the contribution of several sensory modalities to the muscle activation of the opposite leg provides new insight into the sensorimotor control mechanisms utilized in human walking, including the role of supra-spinal neural circuits in inter-leg coordination. Based on these insights, a model is created which relates the unilateral deflection of the walking surface to the resulting neuromuscular activation in the opposite leg. Additionally, case studies with hemiplegic walkers indicate the existence of the observed mechanism in neurologically impaired walkers. The results of this dissertation suggest a novel approach to gait therapy for hemiplegic patients in which desired muscle activity is evoked in the impaired leg by only interacting with the healthy leg. One of the most significant advantages of this approach over current rehabilitation protocols is the safety of the patient since there is no direct manipulation of the impaired leg. Therefore, the methods and results presented in this dissertation represent a potential paradigm shift in robot-assisted gait therapy.
ContributorsSkidmore, Jeffrey Alan (Author) / Artemiadis, Panagiotis (Thesis advisor) / Santello, Marco (Committee member) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2017
Description
All civilization requires some sort of infrastructure to provide an essential service. Roads, bridges, pipelines, railroads, etc. are all critical in maintaining our society, but when they fail, they pose a serious threat to the economy, public safety, and environment. This is why it has become increasingly important to invest in and research the field of Structural Health Monitoring (SHM) to ensure the safety and reliability of our infrastructure. This research paper delves into the optimization of a Lizard-inspired Tube Inspection (LTI) robot, with the primary focus on the inspection side of SHM through the use of Electro Magnetic Acoustic Transducer (EMAT), a Non-Destructive Testing (NDT) method. The robot is designed to inspect power plants piping for damage or defects, and its ability to detect issues early, results in improved plant efficiency, enhanced structural data collection, and increased safety. An iterative, reliable design was constructed by reducing the weight and addressing previous design flaws and then tested. Solidworks was used to calculate theoretical weight, applied stress, and displacements for the design modifications.. The overall reduction in weight was around 12.4% of the previous design. While this research successfully reduced the robot's weight and resolved issues in its design, further optimization is still necessary. Future studies should investigate the finger and friction pad design, robot control, and ways to reduce the reliance on commercial off-the-shelf parts. This will expand the robot’s inspection capabilities, making it applicable in other industries where NDT is critical to ensure structural integrity and safety, such as the pipes in oil and gas refineries, water treatment plants, and chemical processing plants, innovating the way infrastructure is monitored and maintained.
ContributorsMorris-Sjolund, Drake (Author) / Marvi, Hamidreza (Thesis director) / Lee, Hyunglae (Committee member) / Barrett, The Honors College (Contributor)
Created2023-05
Description
Magnetic liquids called ferrofluids have been used in applications ranging from audio speaker cooling and rotary pressure seals to retinal detachment surgery and implantable artificial glaucoma valves. Recently, ferrofluids have been investigated as a material for use in magnetically controllable liquid droplet robotics. Liquid droplet robotics is an emerging technology that aims to apply control theory to manipulate fluid droplets as robotic agents to perform a wide range of tasks. Furthermore, magnetically controlled micro-robotics is another popular area of study where manipulating a magnetic field allows for the control of magnetized micro-robots. Both of these emerging fields have potential for impact toward medical applications: liquid characteristics such as being able to dissolve various compounds, be injected via a needle, and the potential for the human body to automatically filter and remove a liquid droplet robot, make liquid droplet robots advantageous for medical applications; while the ability to remotely control the torques and forces on an untethered microrobot via modulating the magnetic field and gradient is also highly advantageous. The research described in this dissertation explores applications and methods for the electromagnetic control of ferrofluid droplet robots. First, basic electrical components built from fluidic channels containing ferrofluid are made remotely tunable via the placement of ferrofluid within the channel. Second, a ferrofluid droplet is shown to be fully controllable in position, stretch direction, and stretch length in two dimensions using proportional-integral-derivative (PID) controllers. Third, control of a ferrofluid’s position, stretch direction, and stretch length is extended to three dimensions, and control gains are optimized via a Bayesian optimization process to achieve higher accuracy. Finally, magnetic control of both single and multiple ferrofluid droplets in two dimensions is investigated via a visual model predictive control approach based on machine learning. These achievements take both liquid droplet robotics and magnetic micro-robotics fields several steps closer toward real-world medical applications such as embedded soft electronic health monitors, liquid-droplet-robot-based drug delivery, and automated magnetically actuated surgeries.
ContributorsAhmed, Reza James (Author) / Marvi, Hamidreza (Thesis advisor) / Espanol, Malena (Committee member) / Rajagopalan, Jagannathan (Committee member) / Zhuang, Houlong (Committee member) / Xu, Zhe (Committee member) / Arizona State University (Publisher)
Created2022
Description
In this paper, we discuss the methods and requirements to simulate a soft bodied beam using traditional rigid body kinematics to produce motion inspired by eels. Eels produce a form of undulatory locomotion called anguilliform locomotion that propagates waves throughout the entire body. The system that we are analyzing is a flexible 3D printed beam being actively driven by a servo motor. Using the simulation, we also analyze different parameters for these spines to maximize the linear speed of the system.
ContributorsKwan, Anson (Author) / Aukes, Daniel (Thesis director) / Marvi, Hamidreza (Committee member) / Barrett, The Honors College (Contributor) / Engineering Programs (Contributor)
Created2022-05
Description
Machine learning has demonstrated great potential across a wide range of applications such as computer vision, robotics, speech recognition, drug discovery, material science, and physics simulation. Despite its current success, however, there are still two major challenges for machine learning algorithms: limited robustness and generalizability.
The robustness of a neural network is defined as the stability of the network output under small input perturbations. It has been shown that neural networks are very sensitive to input perturbations, and the prediction from convolutional neural networks can be totally different for input images that are visually indistinguishable to human eyes. Based on such property, hackers can reversely engineer the input to trick machine learning systems in targeted ways. These adversarial attacks have shown to be surprisingly effective, which has raised serious concerns over safety-critical applications like autonomous driving. In the meantime, many established defense mechanisms have shown to be vulnerable under more advanced attacks proposed later, and how to improve the robustness of neural networks is still an open question.
The generalizability of neural networks refers to the ability of networks to perform well on unseen data rather than just the data that they were trained on. Neural networks often fail to carry out reliable generalizations when the testing data is of different distribution compared with the training one, which will make autonomous driving systems risky under new environment. The generalizability of neural networks can also be limited whenever there is a scarcity of training data, while it can be expensive to acquire large datasets either experimentally or numerically for engineering applications, such as material and chemical design.
In this dissertation, we are thus motivated to improve the robustness and generalizability of neural networks. Firstly, unlike traditional bottom-up classifiers, we use a pre-trained generative model to perform top-down reasoning and infer the label information. The proposed generative classifier has shown to be promising in handling input distribution shifts. Secondly, we focus on improving the network robustness and propose an extension to adversarial training by considering the transformation invariance. Proposed method improves the robustness over state-of-the-art methods by 2.5% on MNIST and 3.7% on CIFAR-10. Thirdly, we focus on designing networks that generalize well at predicting physics response. Our physics prior knowledge is used to guide the designing of the network architecture, which enables efficient learning and inference. Proposed network is able to generalize well even when it is trained with a single image pair.
The robustness of a neural network is defined as the stability of the network output under small input perturbations. It has been shown that neural networks are very sensitive to input perturbations, and the prediction from convolutional neural networks can be totally different for input images that are visually indistinguishable to human eyes. Based on such property, hackers can reversely engineer the input to trick machine learning systems in targeted ways. These adversarial attacks have shown to be surprisingly effective, which has raised serious concerns over safety-critical applications like autonomous driving. In the meantime, many established defense mechanisms have shown to be vulnerable under more advanced attacks proposed later, and how to improve the robustness of neural networks is still an open question.
The generalizability of neural networks refers to the ability of networks to perform well on unseen data rather than just the data that they were trained on. Neural networks often fail to carry out reliable generalizations when the testing data is of different distribution compared with the training one, which will make autonomous driving systems risky under new environment. The generalizability of neural networks can also be limited whenever there is a scarcity of training data, while it can be expensive to acquire large datasets either experimentally or numerically for engineering applications, such as material and chemical design.
In this dissertation, we are thus motivated to improve the robustness and generalizability of neural networks. Firstly, unlike traditional bottom-up classifiers, we use a pre-trained generative model to perform top-down reasoning and infer the label information. The proposed generative classifier has shown to be promising in handling input distribution shifts. Secondly, we focus on improving the network robustness and propose an extension to adversarial training by considering the transformation invariance. Proposed method improves the robustness over state-of-the-art methods by 2.5% on MNIST and 3.7% on CIFAR-10. Thirdly, we focus on designing networks that generalize well at predicting physics response. Our physics prior knowledge is used to guide the designing of the network architecture, which enables efficient learning and inference. Proposed network is able to generalize well even when it is trained with a single image pair.
ContributorsYao, Houpu (Author) / Ren, Yi (Thesis advisor) / Liu, Yongming (Committee member) / Li, Baoxin (Committee member) / Yang, Yezhou (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2019
Description
Vehicles traverse granular media through complex reactions with large numbers of small particles. Many approaches rely on empirical trends derived from wheeled vehicles in well-characterized media. However, the environments of numerous bodies such as Mars or the moon are primarily composed of fines called regolith which require different design considerations. This dissertation discusses research aimed at understanding the role and function of empirical, computational, and theoretical granular physics approaches as they apply to helical geometries, their envelope of applicability, and the development of new laws. First, a static Archimedes screw submerged in granular material (glass beads) is analyzed using two methods: Granular Resistive Force Theory (RFT), an empirically derived set of equations based on fluid dynamic superposition principles, and Discrete element method (DEM) simulations, a particle modeling software. Dynamic experiments further confirm the computational method with multi-body dynamics (MBD)-DEM co-simulations. Granular Scaling Laws (GSL), a set of physics relationships based on non-dimensional analysis, are utilized for the gravity-modified environments. A testing chamber to contain a lunar analogue, BP-1, is developed and built. An investigation of straight and helical grousered wheels in both silica sand and BP-1 is performed to examine general GSL applicability for lunar purposes. Mechanical power draw and velocity prediction by GSL show non-trivial but predictable deviation. BP-1 properties are characterized and applied to an MBD-DEM environment for the first time. MBD-DEM simulation results between Earth gravity and lunar gravity show good agreement with theoretical predictions for both power and velocity. The experimental deviation is further investigated and found to have a mass-dependant component driven by granular sinkage and engagement. Finally, a robust set of helical granular scaling laws (HGSL) are derived. The granular dynamics scaling of three-dimensional screw-driven mobility is reduced to a similar theory as wheeled scaling laws, provided the screw is radially continuous. The new laws are validated in BP-1 with results showing very close agreement to predictions. A gravity-variant version of these laws is validated with MBD-DEM simulations. The results of the dissertation suggest GSL, HGSL, and MBD-DEM give reasonable approximations for use in lunar environments to predict rover mobility given adequate granular engagement.
ContributorsThoesen, Andrew Lawrence (Author) / Marvi, Hamidreza (Thesis advisor) / Berman, Spring (Committee member) / Emady, Heather (Committee member) / Lee, Hyunglae (Committee member) / Klesh, Andrew (Committee member) / Arizona State University (Publisher)
Created2019
Description
Granular materials demonstrate complexity in many physical attributes with various shapes and sizes, varying from several centimeters down to less than a few microns. Some materials are highly cohesive, while others are free-flowing. Despite such complexity in their physical properties, they are extremely important in industries dealing with bulk materials. Through this research, the factors affecting flowability of particulate solids and their interaction with projectiles were explored. In Part I, a novel set of characterization tools to relate various granular material properties to their flow behavior in confined and unconfined environments was investigated. Through this work, a thorough characterization study to examine the effects of particle size, particle size distribution, and moisture on bulk powder flowability were proposed. Additionally, a mathematical model to predict the flow function coefficient (FFC) was developed, based on the surface mean diameter and moisture level, which can serve as a flowability descriptor.
Part II of this research focuses on the impact dynamics of low velocity projectiles on granular media. Interaction of granular media with external foreign bodies occurs in everyday events like a human footprint on the beach. Several studies involving numerical and experimental methods have focused on the study of impact dynamics in both dry and wet granular media. However, most of the studies involving impact dynamics considered spherical projectiles under different conditions, while practical models should involve more complex, realistic shapes. Different impacting geometries with conserved density, volume, and velocity on a granular bed may experience contrasting drag forces upon penetration. This is due to the difference in the surface areas coming into contact with the granular media. In this study, a set of non-spherical geometries comprising cuboids, cylinders, hexagonal prisms and triangular prisms with constant density, volume, and impact velocities, were released onto a loosely packed, non-cohesive, dry granular bed. From these experimental results, a model to determine the penetration depth of projectiles upon impact was developed and how it is influenced by the release height and surface area of the projectiles in contact with the granular media was studied.
ContributorsVajrala, Spandana (Author) / Emady, Heather N (Thesis advisor) / Marvi, Hamidreza (Committee member) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2021
Description
This thesis presents a process by which a controller used for collective transport tasks is qualitatively studied and probed for presence of undesirable equilibrium states that could entrap the system and prevent it from converging to a target state. Fields of study relevant to this project include dynamic system modeling, modern control theory, script-based system simulation, and autonomous systems design. Simulation and computational software MATLAB and Simulink® were used in this thesis.
To achieve this goal, a model of a swarm performing a collective transport task in a bounded domain featuring convex obstacles was simulated in MATLAB/ Simulink®. The closed-loop dynamic equations of this model were linearized about an equilibrium state with angular acceleration and linear acceleration set to zero. The simulation was run over 30 times to confirm system ability to successfully transport the payload to a goal point without colliding with obstacles and determine ideal operating conditions by testing various orientations of objects in the bounded domain. An additional purely MATLAB simulation was run to identify local minima of the Hessian of the navigation-like potential function. By calculating this Hessian periodically throughout the system’s progress and determining the signs of its eigenvalues, a system could check whether it is trapped in a local minimum, and potentially dislodge itself through implementation of a stochastic term in the robot controllers. The eigenvalues of the Hessian calculated in this research suggested the model local minima were degenerate, indicating an error in the mathematical model for this system, which likely incurred during linearization of this highly nonlinear system.
To achieve this goal, a model of a swarm performing a collective transport task in a bounded domain featuring convex obstacles was simulated in MATLAB/ Simulink®. The closed-loop dynamic equations of this model were linearized about an equilibrium state with angular acceleration and linear acceleration set to zero. The simulation was run over 30 times to confirm system ability to successfully transport the payload to a goal point without colliding with obstacles and determine ideal operating conditions by testing various orientations of objects in the bounded domain. An additional purely MATLAB simulation was run to identify local minima of the Hessian of the navigation-like potential function. By calculating this Hessian periodically throughout the system’s progress and determining the signs of its eigenvalues, a system could check whether it is trapped in a local minimum, and potentially dislodge itself through implementation of a stochastic term in the robot controllers. The eigenvalues of the Hessian calculated in this research suggested the model local minima were degenerate, indicating an error in the mathematical model for this system, which likely incurred during linearization of this highly nonlinear system.
ContributorsBaye-Wallace, Lily Catherine (Author) / Berman, Spring (Thesis director) / Marvi, Hamidreza (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / School of Music, Dance and Theatre (Contributor) / School for Engineering of Matter,Transport & Enrgy (Contributor) / Barrett, The Honors College (Contributor)
Created2020-12
ContributorsKwan, Anson (Author) / Aukes, Daniel (Thesis director) / Marvi, Hamidreza (Committee member) / Barrett, The Honors College (Contributor) / Engineering Programs (Contributor)
Created2022-05