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.
In this research, computational optical modeling is also introduced to design the Si nanostructure, specifically nanopillars (NPs) with a desired period and dimension. The optical properties of Si NP are calculated with two different optical modeling techniques, which are the rigorous coupled wave analysis (RCWA) and finite-difference time-domain (FDTD) methods. By using these two different optical modeling techniques, the optical properties of Si NPs with different periods and dimensions have been investigated to design ideal Si NP which can be potentially used for thin c-Si solar cell applications. From the results of the computational and experimental work, it was observed that low aspect ratio Si NPs fabricated in a periodic hexagonal array can provide highly enhanced light absorption for the target spectral range (600 ~ 1100nm), which is attributed to (1) the effective confinement of resonant scattering within the Si NP and (2) increased high order diffraction of transmitted light providing an extended absorption length. From the research, therefore, it is successfully demonstrated that the nano-fabrication process with SNS lithography can offer enhanced lithographical accuracy to fabricate desired Si nanostructures which can realize enhanced light absorption for thin Si solar cell.