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- All Subjects: Aerospace Engineering
- Resource Type: Text
- Status: Published
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
Unmanned aerial vehicles have received increased attention in the last decade due to their versatility, as well as the availability of inexpensive sensors (e.g. GPS, IMU) for their navigation and control. Multirotor vehicles, specifically quadrotors, have formed a fast growing field in robotics, with the range of applications spanning from surveil- lance and reconnaissance to agriculture and large area mapping. Although in most applications single quadrotors are used, there is an increasing interest in architectures controlling multiple quadrotors executing a collaborative task. This thesis introduces a new concept of control involving more than one quadrotors, according to which two quadrotors can be physically coupled in mid-flight. This concept equips the quadro- tors with new capabilities, e.g. increased payload or pursuit and capturing of other quadrotors. A comprehensive simulation of the approach is built to simulate coupled quadrotors. The dynamics and modeling of the coupled system is presented together with a discussion regarding the coupling mechanism, impact modeling and additional considerations that have been investigated. Simulation results are presented for cases of static coupling as well as enemy quadrotor pursuit and capture, together with an analysis of control methodology and gain tuning. Practical implementations are introduced as results show the feasibility of this design.
ContributorsLarsson, Daniel (Author) / Artemiadis, Panagiotis (Thesis advisor) / Marvi, Hamidreza (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2016
Description
Building and optimizing a design for deformable media can be extremely costly. However, granular scaling laws enable the ability to predict system velocity and mobility
power consumption by testing at a smaller scale in the same environment. The validity of
the granular scaling laws for arbitrarily shaped wheels and screws were evaluated in
materials like silica sand and BP-1, a lunar simulant. Different wheel geometries, such as
non-grousered and straight and bihelically grousered wheels were created and tested
using 3D printed technologies. Using the granular scaling laws and the empirical data
from initial experiments, power and velocity were predicted for a larger scaled version
then experimentally validated on a dynamic mobility platform. Working with granular
media has high variability in material properties depending on initial environmental
conditions, so particular emphasis was placed on consistency in the testing methodology.
Through experiments, these scaling laws have been validated with defined use cases and
limitations.
ContributorsMcbryan, Teresa (Author) / Marvi, Hamidreza (Thesis advisor) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Arizona State University (Publisher)
Created2022
Description
A novel underwater, open source, and configurable vehicle that mimics and leverages advances in quad-copter controls and dynamics, called the uDrone, was designed, built and tested. This vehicle was developed to aid coral reef researchers in collecting underwater spectroscopic data for the purpose of monitoring coral reef health. It is designed with an on-board integrated sensor system to support both automated navigation in close proximity to reefs and environmental observation. Additionally, the vehicle can serve as a testbed for future research in the realm of programming for autonomous underwater navigation and data collection, given the open-source simulation and software environment in which it was developed. This thesis presents the motivation for and design components of the new vehicle, a model governing vehicle dynamics, and the results of two proof-of-concept simulation for automated control.
ContributorsGoldman, Alex (Author) / Das, Jnaneshwar (Thesis advisor) / Asner, Greg (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2020
Description
This thesis aims to design of wings for a laminate biped robot for providing locomotion stabilization during jump gliding. The wings are designed to collapse down during the jumping phase to maximize jump height and deployed back for gliding phase using anisotropic buckling in tape spring hinges. The project aims to develop a reliable dynamics model which can be utilized for design and evaluation of optimized systems for jump-gliding. The aerodynamic simulations are run on a vortex-lattice code which provides numeric simulations of the defined geometric bodies. The aerodynamic simulations assist in improving the design parameters such as planform, camber and twist to achieve the best possible Coefficient of Lift for maximizing glide distance. The aerodynamic simulation output is then plugged into a dynamics model built in Python, which is validated and correlated with experimental testing of a key wing designs. The experimental results are then utilized to improve the dynamics model and obtain better designs for improved performance. The simulation model informs the aerodynamic design of wings for sustaining glide for the biped platform and maximizing glide length to increase range.
ContributorsGadekar, Vipul (Author) / Takahashi, Timothy (Thesis advisor) / Aukes, Daniel (Thesis advisor) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2020