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- All Subjects: Mechanical Engineering
- Genre: Doctoral Dissertation
Description
One type of assistive device for the blind has attempted to convert visual information into information that can be perceived through another sense, such as touch or hearing. A vibrotactile haptic display assistive device consists of an array of vibrating elements placed against the skin, allowing the blind individual to receive visual information through touch. However, these approaches have two significant technical challenges: large vibration element size and the number of microcontroller pins required for vibration control, both causing excessively low resolution of the device. Here, I propose and investigate a type of high-resolution vibrotactile haptic display which overcomes these challenges by utilizing a ‘microbeam’ as the vibrating element. These microbeams can then be actuated using only one microcontroller pin connected to a speaker or surface transducer. This approach could solve the low-resolution problem currently present in all haptic displays. In this paper, the results of an investigation into the manufacturability of such a device, simulation of the vibrational characteristics, and prototyping and experimental validation of the device concept are presented. The possible reasons of the frequency shift between the result of the forced or free response of beams and the frequency calculated based on a lumped mass approximation are investigated. It is found that one of the important reasons for the frequency shift is the size effect, the dependency of the elastic modulus on the size and kind of material. This size effect on A2 tool steel for Micro-Meso scale cantilever beams for the proposed system is investigated.
ContributorsWi, Daehan (Author) / SODEMANN, ANGELA A (Thesis advisor) / Redkar, Sangram (Committee member) / McDaniel, Troy (Committee member) / Arizona State University (Publisher)
Created2019
Description
Wearable robotics has gained huge popularity in recent years due to its wide applications in rehabilitation, military, and industrial fields. The weakness of the skeletal muscles in the aging population and neurological injuries such as stroke and spinal cord injuries seriously limit the abilities of these individuals to perform daily activities. Therefore, there is an increasing attention in the development of wearable robots to assist the elderly and patients with disabilities for motion assistance and rehabilitation. In military and industrial sectors, wearable robots can increase the productivity of workers and soldiers. It is important for the wearable robots to maintain smooth interaction with the user while evolving in complex environments with minimum effort from the user. Therefore, the recognition of the user's activities such as walking or jogging in real time becomes essential to provide appropriate assistance based on the activity.
This dissertation proposes two real-time human activity recognition algorithms intelligent fuzzy inference (IFI) algorithm and Amplitude omega ($A \omega$) algorithm to identify the human activities, i.e., stationary and locomotion activities. The IFI algorithm uses knee angle and ground contact forces (GCFs) measurements from four inertial measurement units (IMUs) and a pair of smart shoes. Whereas, the $A \omega$ algorithm is based on thigh angle measurements from a single IMU.
This dissertation also attempts to address the problem of online tuning of virtual impedance for an assistive robot based on real-time gait and activity measurement data to personalize the assistance for different users. An automatic impedance tuning (AIT) approach is presented for a knee assistive device (KAD) in which the IFI algorithm is used for real-time activity measurements. This dissertation also proposes an adaptive oscillator method known as amplitude omega adaptive oscillator ($A\omega AO$) method for HeSA (hip exoskeleton for superior augmentation) to provide bilateral hip assistance during human locomotion activities. The $A \omega$ algorithm is integrated into the adaptive oscillator method to make the approach robust for different locomotion activities. Experiments are performed on healthy subjects to validate the efficacy of the human activities recognition algorithms and control strategies proposed in this dissertation. Both the activity recognition algorithms exhibited higher classification accuracy with less update time. The results of AIT demonstrated that the KAD assistive torque was smoother and EMG signal of Vastus Medialis is reduced, compared to constant impedance and finite state machine approaches. The $A\omega AO$ method showed real-time learning of the locomotion activities signals for three healthy subjects while wearing HeSA. To understand the influence of the assistive devices on the inherent dynamic gait stability of the human, stability analysis is performed. For this, the stability metrics derived from dynamical systems theory are used to evaluate unilateral knee assistance applied to the healthy participants.
This dissertation proposes two real-time human activity recognition algorithms intelligent fuzzy inference (IFI) algorithm and Amplitude omega ($A \omega$) algorithm to identify the human activities, i.e., stationary and locomotion activities. The IFI algorithm uses knee angle and ground contact forces (GCFs) measurements from four inertial measurement units (IMUs) and a pair of smart shoes. Whereas, the $A \omega$ algorithm is based on thigh angle measurements from a single IMU.
This dissertation also attempts to address the problem of online tuning of virtual impedance for an assistive robot based on real-time gait and activity measurement data to personalize the assistance for different users. An automatic impedance tuning (AIT) approach is presented for a knee assistive device (KAD) in which the IFI algorithm is used for real-time activity measurements. This dissertation also proposes an adaptive oscillator method known as amplitude omega adaptive oscillator ($A\omega AO$) method for HeSA (hip exoskeleton for superior augmentation) to provide bilateral hip assistance during human locomotion activities. The $A \omega$ algorithm is integrated into the adaptive oscillator method to make the approach robust for different locomotion activities. Experiments are performed on healthy subjects to validate the efficacy of the human activities recognition algorithms and control strategies proposed in this dissertation. Both the activity recognition algorithms exhibited higher classification accuracy with less update time. The results of AIT demonstrated that the KAD assistive torque was smoother and EMG signal of Vastus Medialis is reduced, compared to constant impedance and finite state machine approaches. The $A\omega AO$ method showed real-time learning of the locomotion activities signals for three healthy subjects while wearing HeSA. To understand the influence of the assistive devices on the inherent dynamic gait stability of the human, stability analysis is performed. For this, the stability metrics derived from dynamical systems theory are used to evaluate unilateral knee assistance applied to the healthy participants.
ContributorsChinimilli, Prudhvi Tej (Author) / Redkar, Sangram (Thesis advisor) / Zhang, Wenlong (Thesis advisor) / Sugar, Thomas G. (Committee member) / Lee, Hyunglae (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2018
Description
Wearable robotics is a growing sector in the robotics industry, they can increase the productivity of workers and soldiers and can restore some of the lost function to people with disabilities. Wearable robots should be comfortable, easy to use, and intuitive. Robust control methods are needed for wearable robots that assist periodic motion.
This dissertation studies a phase based oscillator constructed with a second order dynamic system and a forcing function based on the phase angle of the system. This produces a bounded control signal that can alter the damping and stiffens properties of the dynamic system. It is shown analytically and experimentally that it is stable and robust. It can handle perturbations remarkably well. The forcing function uses the states of the system to produces stable oscillations. Also, this work shows the use of the phase based oscillator in wearable robots to assist periodic human motion focusing on assisting the hip motion. One of the main problems to assist periodic motion properly is to determine the frequency of the signal. The phase oscillator eliminates this problem because the signal always has the correct frequency. The input requires the position and velocity of the system. Additionally, the simplicity of the controller allows for simple implementation.
This dissertation studies a phase based oscillator constructed with a second order dynamic system and a forcing function based on the phase angle of the system. This produces a bounded control signal that can alter the damping and stiffens properties of the dynamic system. It is shown analytically and experimentally that it is stable and robust. It can handle perturbations remarkably well. The forcing function uses the states of the system to produces stable oscillations. Also, this work shows the use of the phase based oscillator in wearable robots to assist periodic human motion focusing on assisting the hip motion. One of the main problems to assist periodic motion properly is to determine the frequency of the signal. The phase oscillator eliminates this problem because the signal always has the correct frequency. The input requires the position and velocity of the system. Additionally, the simplicity of the controller allows for simple implementation.
ContributorsDe la Fuente Valadez, Juan Oziel (Author) / Sugar, Thomas G. (Committee member) / Redkar, Sangram (Committee member) / Berman, Spring (Committee member) / Artemiadis, Panagiotis (Committee member) / Schroeder, Kyle A (Committee member) / Arizona State University (Publisher)
Created2016
Description
This research seeks to present the design and testing of exoskeletons capable of assisting with walking gait, squatting, and fall prevention activities. The dissertation introduces wearable
robotics and exoskeletons and then progresses into specific applications and developments in the
targeted field. Following the introduction, chapters present and discuss different wearable
exoskeletons built to address known issues with workers and individuals with increased risk of fall.
The presentation is concluded by an overall analysis of the resulting developments and identifying
future work in the field.
ContributorsOlson, Jason Stewart (Author) / Redkar, Sangram (Thesis advisor) / Sugar, Thomas (Committee member) / Honeycutt, Claire (Committee member) / Arizona State University (Publisher)
Created2021
Description
The inherent behavior of many real world applications tends to exhibit complex or chaotic patterns. A novel technique to reduce and analyze such complex systems is introduced in this work, and its applications to multiple perturbed systems are discussed comprehensively. In this work, a unified approach between the Floquet theory for time periodic systems and the Poincare theory of Normal Forms is proposed to analyze time varying systems. The proposed unified approach is initially verified for linear time periodic systems with the aid of an intuitive state augmentation and the method of Time Independent Normal Forms (TINF). This approach also resulted in the closed form expressions for the State Transition Matrix (STM) and Lyapunov-Floquet (L-F) transformation for linear time periodic systems. The application of theory towards stability analysis is further demonstrated with the system of Suction Stabilized Floating (SSF) platform. Additionally, multiple control strategies are discussed and implemented to drive an unstable time periodic system to a desired stable point or orbit efficiently and optimally. The computed L-F transformation is further utilized to analyze nonlinear and externally excited systems with deterministic and stochastic time periodic coefficients. The central theme of this work is to verify the extension of Floquet theory towards time varying systems with periodic coefficients comprising of incommensurate frequencies or quasi-periodic systems. As per Floquet theory, a Lyapunov-Perron (L-P) transformation converts a time-varying quasi-periodic system to a time-invariant form. A class of commutative quasi-periodic systems is introduced to demonstrate the proposed theory and its applications analytically. An extension of the proposed unified approach towards analyzing the linear quasi-periodic system is observed to provide good results, computationally less complex and widely applicable for strongly excited systems. The computed L-P transformation using the unified theory is applied to analyze both commutative and non-commutative linear quasi-periodic systems with nonlinear terms and external excitation terms. For highly nonlinear quasi-periodic systems, the implementation of multiple order reduction techniques and their performance comparisons are illustrated in this work. Finally, the robustness and stability analysis of nonlinearly perturbed and stochastically excited quasi-periodic systems are performed using Lyapunov's direct method and Infante's approach.
ContributorsCherangara Subramanian, Susheelkumar (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Bradley (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2021
Description
In this research work, a novel control system strategy for the robust control of an unmanned ground vehicle is proposed. This strategy is motivated by efforts to mitigate the problem for scenarios in which the human operator is unable to properly communicate with the vehicle. This novel control system strategy consisted of three major components: I.) Two independent intelligent controllers, II.) An intelligent navigation system, and III.) An intelligent controller tuning unit. The inner workings of the first two components are based off the Brain Emotional Learning (BEL), which is a mathematical model of the Amygdala-Orbitofrontal, a region in mammalians brain known to be responsible for emotional learning. Simulation results demonstrated the implementation of the BEL model to be very robust, efficient, and adaptable to dynamical changes in its application as controller and as a sensor fusion filter for an unmanned ground vehicle. These results were obtained with significantly less computational cost when compared to traditional methods for control and sensor fusion. For the intelligent controller tuning unit, the implementation of a human emotion recognition system was investigated. This system was utilized for the classification of driving behavior. Results from experiments showed that the affective states of the driver are accurately captured. However, the driver's affective state is not a good indicator of the driver's driving behavior. As a result, an alternative method for classifying driving behavior from the driver's brain activity was explored. This method proved to be successful at classifying the driver's behavior. It obtained results comparable to the common approach through vehicle parameters. This alternative approach has the advantage of directly classifying driving behavior from the driver, which is of particular use in UGV domain because the operator's information is readily available. The classified driving mode was used tune the controllers' performance to a desired mode of operation. Such qualities are required for a contingency control system that would allow the vehicle to operate with no operator inputs.
ContributorsVargas-Clara, Alvaro (Author) / Redkar, Sangram (Thesis advisor) / McKenna, Anna (Committee member) / Cooke, Nancy J. (Committee member) / Arizona State University (Publisher)
Created2015