ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
Humans desire compliant robots to safely interact in dynamic environments
associated with daily activities. As surface electromyography non-invasively measures
limb motion intent and correlates with joint stiness during co-contractions,
it has been identied as a candidate for naturally controlling such robots. However,
state-of-the-art myoelectric interfaces have struggled to achieve both enhanced
functionality and long-term reliability. As demands in myoelectric interfaces trend
toward simultaneous and proportional control of compliant robots, robust processing
of multi-muscle coordinations, or synergies, plays a larger role in the success of the
control scheme. This dissertation presents a framework enhancing the utility of myoelectric
interfaces by exploiting motor skill learning and
exible muscle synergies for
reliable long-term simultaneous and proportional control of multifunctional compliant
robots. The interface is learned as a new motor skill specic to the controller,
providing long-term performance enhancements without requiring any retraining or
recalibration of the system. Moreover, the framework oers control of both motion
and stiness simultaneously for intuitive and compliant human-robot interaction. The
framework is validated through a series of experiments characterizing motor learning
properties and demonstrating control capabilities not seen previously in the literature.
The results validate the approach as a viable option to remove the trade-o
between functionality and reliability that have hindered state-of-the-art myoelectric
interfaces. Thus, this research contributes to the expansion and enhancement of myoelectric
controlled applications beyond commonly perceived anthropomorphic and
\intuitive control" constraints and into more advanced robotic systems designed for
everyday tasks.
With the correct permits in place, further research can explore how different UAS network topologies behave in an urban environment when implemented with off the shelf UAS hardware. In addition to testing different network topologies, this thesis covers the implementation of building a secure, scalable system using modern cloud computation tools and services capable of supporting a variable number of UAS. The system also supports the end-to-end simulation of the system considering factors such as battery life and realistic UAS kinematics. The implementation of the system leads to new findings needed to deploy UAS fleets in urban environments.
in space and time to meet different needs. Approximately 2 million Americans live
with an amputation with most of those amputations being of the lower limbs. To
advance current state-of-the-art lower limb prosthetic devices, it is necessary to adapt
performance at a level of intelligence seen in human walking. As such, this thesis
focuses on the mechanisms involved during human walking, while transitioning from
rigid to compliant surfaces such as from pavement to sand, grass or granular media.
Utilizing a unique tool, the Variable Stiffness Treadmill (VST), as the platform for
human walking, rigid to compliant surface transitions are simulated. The analysis of
muscular activation during the transition from rigid to different compliant surfaces
reveals specific anticipatory muscle activation that precedes stepping on a compliant
surface. There is also an indication of varying responses for different surface stiffness
levels. This response is observed across subjects. Results obtained are novel and
useful in establishing a framework for implementing control algorithm parameters to
improve powered ankle prosthesis. With this, it is possible for the prosthesis to adapt
to a new surface and therefore resulting in a more robust smart powered lower limb
prosthesis.
water. Due to the complicated fluid dynamics of this process, the forces applied on the
water’s surface cannot be measured using traditional methods. This thesis presents a
novel technique of measuring the forces using a fluid dynamic force platform (FDFP),
a light, rigid box immersed in water. This platform, along with a motion capture
system, can be used to characterize the kinematics and dynamics of a basilisk lizard
running on water. This could ultimately lead to robots that can run on water in a
similar manner.
their performances are compared. Equations that can be used to calculate observer gains are mentioned. Different methods that can be used to implement observers for time-delay systems are illustrated. Various stable and unstable systems are used and H∞ bounds are calculated using these observer designing methods. Delays are assumed to be known constants for all systems. H∞ gains are calculated numerically using disturbance signals and performances of observers are compared.
The primary goal of this thesis is to implement the observer for Time Delay Systems designed using SOS and compare its performance with existing H∞ optimal observers. These observers are more general than other observers for time-delay systems as they make corrections to the delayed state as well along with the present state. The observer dynamics can be represented by an ODE coupled with a PDE. Results shown in this thesis show that this type of observers performs better than other H∞ observers. Sub-optimal observer-based state feedback system is also generated and simulated using the SOS observer. The simulation results show that the closed loop system converges very quickly, and the observer can be used to design full state-feedback closed loop system.
A series elastic actuator is one of the many actuation mechanisms employed in exoskeletons. In this mechanism a torsion spring is used between the actuator and human joint. It serves as torque sensor and energy buffer, making it compact and
safe.
A version of knee exoskeleton was developed using the SEA mechanism. It uses worm gear and spur gear combination to amplify the assistive torque generated from the DC motor. It weighs 1.57 kg and provides a maximum assistive torque of 11.26 N·m. It can be used as a rehabilitation device for patients affected with knee joint impairment.
A new version of exoskeleton design is proposed as an improvement over the first version. It consists of components such as brushless DC motor and planetary gear that are selected to meet the design requirements and biomechanical considerations. All the other components such as bevel gear and torsion spring are selected to be compatible with the exoskeleton. The frame of the exoskeleton is modeled in SolidWorks to be modular and easy to assemble. It is fabricated using sheet metal aluminum. It is designed to provide a maximum assistive torque of 23 N·m, two times over the present exoskeleton. A simple brace is 3D printed, making it easy to wear and use. It weighs 2.4 kg.
The exoskeleton is equipped with encoders that are used to measure spring deflection and motor angle. They act as sensors for precise control of the exoskeleton.
An impedance-based control is implemented using NI MyRIO, a FPGA based controller. The motor is controlled using a motor driver and powered using an external battery source. The bench tests and walking tests are presented. The new version of exoskeleton is compared with first version and state of the art devices.