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Description
Oscillatory perturbations with varying amplitudes and frequencies have been found to significantly affect human standing balance. However, previous studies have only applied perturbation in either the anterior-posterior (AP) or the medio-lateral (ML) directions. Little is currently known about the impacts of 2D oscillatory perturbations on postural stability, which are more commonly seen in daily life (i.e., while traveling on trains, ships, etc.). This study investigated the effects of applying 2D perturbations vs 1D perturbations on standing stability, and how increasing the frequency and amplitude of perturbation impacts postural stability. A dual-axis robotic platform was utilized to simulate various oscillatory perturbations and evaluate standing postural stability. Fifteen young healthy subjects were recruited to perform quiet stance on the platform. Impacts of perturbation direction (i.e., 1D versus 2D), amplitude, and frequency on postural stability were investigated by analyzing different stability measures, specifically AP/ML/2D Center-of-Pressure (COP) path length, AP/ML/2D Time-to-Boundary (TtB), and sway area. Standing postural stability was compromised more by 2D perturbations than 1D perturbations, evidenced by a significant increase in COP path length and sway area and decrease in TtB. Further, the stability decreased as 2D perturbation amplitude and frequency increased. A significant increase in COP path length and decrease in TtB were consistently observed as the 2D perturbation amplitude and frequency increased. However, sway area showed a considerable increase only with increasing perturbation amplitude but not with increasing frequency.
ContributorsBerrett, Lauren Ann (Author) / Lee, Hyunglae (Thesis director) / Peterson, Daniel (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / School of International Letters and Cultures (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
The theme for this work is the development of fast numerical algorithms for sparse optimization as well as their applications in medical imaging and source localization using sensor array processing. Due to the recently proposed theory of Compressive Sensing (CS), the $\ell_1$ minimization problem attracts more attention for its ability to exploit sparsity. Traditional interior point methods encounter difficulties in computation for solving the CS applications. In the first part of this work, a fast algorithm based on the augmented Lagrangian method for solving the large-scale TV-$\ell_1$ regularized inverse problem is proposed. Specifically, by taking advantage of the separable structure, the original problem can be approximated via the sum of a series of simple functions with closed form solutions. A preconditioner for solving the block Toeplitz with Toeplitz block (BTTB) linear system is proposed to accelerate the computation. An in-depth discussion on the rate of convergence and the optimal parameter selection criteria is given. Numerical experiments are used to test the performance and the robustness of the proposed algorithm to a wide range of parameter values. Applications of the algorithm in magnetic resonance (MR) imaging and a comparison with other existing methods are included. The second part of this work is the application of the TV-$\ell_1$ model in source localization using sensor arrays. The array output is reformulated into a sparse waveform via an over-complete basis and study the $\ell_p$-norm properties in detecting the sparsity. An algorithm is proposed for minimizing a non-convex problem. According to the results of numerical experiments, the proposed algorithm with the aid of the $\ell_p$-norm can resolve closely distributed sources with higher accuracy than other existing methods.
ContributorsShen, Wei (Author) / Mittlemann, Hans D (Thesis advisor) / Renaut, Rosemary A. (Committee member) / Jackiewicz, Zdzislaw (Committee member) / Gelb, Anne (Committee member) / Ringhofer, Christian (Committee member) / Arizona State University (Publisher)
Created2011
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
In recent years, networked systems have become prevalent in communications, computing, sensing, and many other areas. In a network composed of spatially distributed agents, network-wide synchronization of information about the physical environment and the network configuration must be maintained using measurements collected locally by the agents. Registration is a process for connecting the coordinate frames of multiple sets of data. This poses numerous challenges, particularly due to availability of direct communication only between neighboring agents in the network. These are exacerbated by uncertainty in the measurements and also by imperfect communication links. This research explored statistically based registration in a sensor network. The approach developed exploits measurements of offsets formed as differences of state values between pairs of agents that share a link in the network graph. It takes into account that the true offsets around any closed cycle in the network graph must sum to zero.
ContributorsPhuong, Shih-Ling (Author) / Cochran, Douglas (Thesis director) / Berman, Spring (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2014-05
Description
This thesis focused on understanding how humans visually perceive swarm behavior through the use of swarm simulations and gaze tracking. The goal of this project was to determine visual patterns subjects display while observing and supervising a swarm as well as determine what swarm characteristics affect these patterns. As an ultimate goal, it was hoped that this research will contribute to optimizing human-swarm interaction for the design of human supervisory controllers for swarms. To achieve the stated goals, two investigations were conducted. First, subjects gaze was tracked while observing a simulated swarm as it moved across the screen. This swarm changed in size, disturbance level in the position of the agents, speed, and path curvature. Second, subjects were asked to play a supervisory role as they watched a swarm move across the screen toward targets. The subjects determined whether a collision would occur and with which target while their responses as well as their gaze was tracked. In the case of an observatory role, a model of human gaze was created. This was embodied in a second order model similar to that of a spring-mass-damper system. This model was similar across subjects and stable. In the case of a supervisory role, inherent weaknesses in human perception were found, such as the inability to predict future position of curved paths. These findings are discussed in depth within the thesis. Overall, the results presented suggest that understanding human perception of swarms offers a new approach to the problem of swarm control. The ability to adapt controls to the strengths and weaknesses could lead to great strides in the reduction of operators in the control of one UAV, resulting in a move towards one man operation of a swarm.
ContributorsWhitton, Elena Michelle (Author) / Artemiadis, Panagiotis (Thesis director) / Berman, Spring (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2015-05
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
Description
Advancements in the field of design and control of lower extremity robotics requires a comprehensive understanding of the underlying mechanics of the human ankle. The ankle joint acts as an essential interface between the neuromuscular system of the body and the physical world, especially during locomotion. This paper investigates how the modulation of ankle stiffness is altered throughout the stance phase of the gait cycle depending on the environment the ankle is interacting with. Ten young healthy subjects with no neurological impairments or history of ankle injury were tested by walking over a robotic platform which collected torque and position data. The platform performed a perturbation on the ankle at 20%, 40%, and 60% of their stance phase in order to estimate ankle stiffness and evaluate if the environment plays a role on its modulation. The platform provided either a rigid environment or a compliant environment in which it was compliant and deflected according to the torque applied to the platform. Subjects adapted in different ways to achieve balance in the different environments. When comparing the environments, subjects modulated their stiffness to either increase, decrease, or remain the same. Notably, stiffness as well as the subjects’ center of pressure was found to increase with time as they transitioned from late loading to terminal stance (heel strike to toe-off) regardless of environmental conditions. This allowed for a model of ankle stiffness to be developed as a function of center of pressure, independent of whether a subject is walking on the rigid or compliant environment. The modulation of stiffness parameters characterized in this study can be used in the design and control of lower extremity robotics which focus on accurate biomimicry of the healthy human ankle. The stiffness characteristics can also be used to help identify particular ankle impairments and to design proper treatment for individuals such as those who have suffered from a stroke or MS. Changing environments is where a majority of tripping incidents occur, which can lead to significant injuries. For this reason, studying healthy ankle behavior in a variety of environments is of particular interest.
ContributorsBliss, Clayton F (Author) / Lee, Hyunglae (Thesis director) / Marvi, Hamid (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
This thesis will cover the basics of 2-dimensional motion of a parafoil system to determine and
design an altitude controller that will result in the parafoil starting at a location and landing within the
accepted bounds of a target location. It will go over the equations of motion, picking out the key
formulas that map out how a parafoil moves, and determine the key inputs in order to get the desired
outcome of a controlled trajectory. The physics found in the equations of motion will be turned into
state space representations that organize it into differential equations that coding software can make
use of to make trajectory calculations. MATLAB is the software used throughout the paper, and all code
used in the thesis paper will be written out for others to check and modify to their desires. Important
aspects of parafoil gliding motion will be discussed and tested with variables such as the natural glide
angle and velocity and the utilization of checkpoints in trajectory controller design. Lastly, the region of
attraction for the controller designed in this thesis paper will be discussed and plotted in order to show
the relationship between the four input variables, x position, y position, velocity, and theta.
The controller utilized in this thesis paper was able to plot a successful flight trajectory from
10m in the air to a target location 50m away. This plot is found in figure 18. The parafoil undershot the
target location by about 9 centimeters (0.18% error). This is an acceptable amount of error and shows
that the controller was a success in controlling the system to reach its target destination. When
compared to the uncontrolled flight in figure 17, the target will only be reached when a controller is
applied to the system.
design an altitude controller that will result in the parafoil starting at a location and landing within the
accepted bounds of a target location. It will go over the equations of motion, picking out the key
formulas that map out how a parafoil moves, and determine the key inputs in order to get the desired
outcome of a controlled trajectory. The physics found in the equations of motion will be turned into
state space representations that organize it into differential equations that coding software can make
use of to make trajectory calculations. MATLAB is the software used throughout the paper, and all code
used in the thesis paper will be written out for others to check and modify to their desires. Important
aspects of parafoil gliding motion will be discussed and tested with variables such as the natural glide
angle and velocity and the utilization of checkpoints in trajectory controller design. Lastly, the region of
attraction for the controller designed in this thesis paper will be discussed and plotted in order to show
the relationship between the four input variables, x position, y position, velocity, and theta.
The controller utilized in this thesis paper was able to plot a successful flight trajectory from
10m in the air to a target location 50m away. This plot is found in figure 18. The parafoil undershot the
target location by about 9 centimeters (0.18% error). This is an acceptable amount of error and shows
that the controller was a success in controlling the system to reach its target destination. When
compared to the uncontrolled flight in figure 17, the target will only be reached when a controller is
applied to the system.
ContributorsTeoharevic, Filip (Author) / Grewal, Anoop (Thesis director) / Lee, Hyunglae (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
As urban populations increase, so does the demand for innovative transportation solutions which reduce traffic congestion, reduce pollution, and reduce inequalities by providing mobility for all kinds of people. One emerging solution is self-driving vehicles, which have been coined as a safer driving method by reducing fatalities due to driving accidents. While completely automated vehicles are still in the testing and development phase, the United Nations predict their full debut by 2030 [1]. While many resources are focusing their time on creating the technology to execute decisions such as the controls, communications, and sensing, engineers often leave ethics as an afterthought. The truth is autonomous vehicles are imperfect systems that will still experience possible crash scenarios even if all systems are working perfectly. Because of this, ethical machine learning must be considered and implemented to avoid an ethical catastrophe which could delay or completely halt future autonomous vehicle development. This paper presents an experiment for determining a more complete view of human morality and how this translates into ideal driving behaviors.
This paper analyzes responses to deviated Trolley Problem scenarios [5] in a simulated driving environment and still images from MIT’s moral machine website [8] to better understand how humans respond to various crashes. Also included is participants driving habits and personal values, however the bulk of that analysis is not included here. The results of the simulation prove that for the most part in driving scenarios, people would rather sacrifice themselves over people outside of the vehicle. The moral machine scenarios prove that self-sacrifice changes as the trend to harm one’s own vehicle was not so strong when passengers were introduced. Further defending this idea is the importance placed on Family Security over any other value.
Suggestions for implementing ethics into autonomous vehicle crashes stem from the results of this experiment but are dependent on more research and greater sample sizes. Once enough data is collected and analyzed, a moral baseline for human’s moral domain may be agreed upon, quantified, and turned into hard rules governing how self-driving cars should act in different scenarios. With these hard rules as boundary conditions, artificial intelligence should provide training and incremental learning for scenarios which cannot be determined by the rules. Finally, the neural networks which make decisions in artificial intelligence must move from their current “black box” state to something more traceable. This will allow researchers to understand why an autonomous vehicle made a certain decision and allow tweaks as needed.
This paper analyzes responses to deviated Trolley Problem scenarios [5] in a simulated driving environment and still images from MIT’s moral machine website [8] to better understand how humans respond to various crashes. Also included is participants driving habits and personal values, however the bulk of that analysis is not included here. The results of the simulation prove that for the most part in driving scenarios, people would rather sacrifice themselves over people outside of the vehicle. The moral machine scenarios prove that self-sacrifice changes as the trend to harm one’s own vehicle was not so strong when passengers were introduced. Further defending this idea is the importance placed on Family Security over any other value.
Suggestions for implementing ethics into autonomous vehicle crashes stem from the results of this experiment but are dependent on more research and greater sample sizes. Once enough data is collected and analyzed, a moral baseline for human’s moral domain may be agreed upon, quantified, and turned into hard rules governing how self-driving cars should act in different scenarios. With these hard rules as boundary conditions, artificial intelligence should provide training and incremental learning for scenarios which cannot be determined by the rules. Finally, the neural networks which make decisions in artificial intelligence must move from their current “black box” state to something more traceable. This will allow researchers to understand why an autonomous vehicle made a certain decision and allow tweaks as needed.
ContributorsBeaulieu, Natalie Nicole (Author) / Berman, Spring (Thesis director) / Cooke, Nancy (Committee member) / Watts College of Public Service & Community Solut (Contributor) / School for Engineering of Matter,Transport & Enrgy (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Each year, the average vehicle contributes 4.6 metric tons of carbon dioxide into the atmosphere [1]. These gases contribute to around 30,000 premature deaths each year [2] and are linked to in the increase in cases of Asthma. Human health is further impacted by the increase of greenhouse gasses in the atmosphere. Rays from the sun travel to the Earth where they are absorbed. Absorbing the sun’s rays heats up the Earth which is then radiated into space. Greenhouse gasses inhibit this process much like the glass walls in a greenhouse. As a result, the temperature of the Earth steadily increases. The greenhouse effect is dangerous because it can be linked to natural disasters, rising ocean levels, and extinction of species. One of the biggest contributors to the greenhouse effect is burning fossil fuels. Powerplants, agriculture, and transportation are some of the largest contributors to the increase of atmospheric carbon dioxide. To mitigate the effects of transportation, car companies have invested into production of alternative and renewable fuels for their products. One of the sources which has gained popularity recently, is the use of electricity to power our vehicles. Tesla has spearheaded the electric car movement and is largely responsible for this beneficial shift. One issue with this approach is that a majority, around 76.3%, of Americans drive alone on their commute [13]. The market in its current state encourages inefficient transportation due to the lack of alternatives. While motorcycles may offer a more eco-friendly and economical approach to cars, many are afraid of potential hazards of using this mode of transportation. The introduction of electric bikes offers an interesting approach to improving this efficiency and safety issue. The wide availability to customers offers an alternative which pushes the traditional distance limits for commuting on a bicycle. Since the market is relatively new, several issues pose challenges to consumers. This research aims to clarify and analyze the electric bike market in order to supply a potential customer with the tools needed to acquire a high quality and reasonably price bike.
ContributorsFriedrich, Collin Anthony (Author) / Lee, Hyunglae (Thesis director) / Lacy, Gerald (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05