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.
Automated planning provides the solution to this problem -- indeed, one of the main motivations that underpinned the beginnings of the field of automated planning was to provide planning support for Shakey the robot with the STRIPS system. For long, however, automated planners suffered from scalability issues that precluded their application to real world, real time robotic systems. Recent decades have seen a gradual abeyance of those issues, and fast planning systems are now the norm rather than the exception. However, some of these advances in speedup and scalability have been achieved by ignoring or abstracting out challenges that real world integrated robotic systems must confront.
In this work, the problem of planning for human-hobot teaming is introduced. The central idea -- the use of automated planning systems as mediators in such human-robot teaming scenarios -- and the main challenges inspired from real world scenarios that must be addressed in order to make such planning seamless are presented: (i) Goals which can be specified or changed at execution time, after the planning process has completed; (ii) Worlds and scenarios where the state changes dynamically while a previous plan is executing; (iii) Models that are incomplete and can be changed during execution; and (iv) Information about the human agent's plan and intentions that can be used for coordination. These challenges are compounded by the fact that the human-robot team must execute in an open world, rife with dynamic events and other agents; and in a manner that encourages the exchange of information between the human and the robot. As an answer to these challenges, implemented solutions and a fielded prototype that combines all of those solutions into one planning system are discussed. Results from running this prototype in real world scenarios are presented, and extensions to some of the solutions are offered as appropriate.
ure of merit (zT) due to quantum connement eects. Improving the eciency of
thermoelectric devices allows for the development of better, more economical waste
heat recovery systems. Such systems may be used as bottoming or co-generation
cycles in conjunction with conventional power cycles to recover some of the wasted
heat. Thermal conductivity measurement systems are an important part of the char-
acterization processes of thermoelectric materials. These systems must possess the
capability of accurately measuring the thermal conductivity of both bulk and thin-lm
samples at dierent ambient temperatures.
This paper discusses the construction, validation, and improvement of a thermal
conductivity measurement platform based on the 3-Omega technique. Room temperature
measurements of thermal conductivity done on control samples with known properties
such as undoped bulk silicon (Si), bulk gallium arsenide (GaAs), and silicon dioxide
(SiO2) thin lms yielded 150 W=m􀀀K, 50 W=m􀀀K, and 1:46 W=m􀀀K respectively.
These quantities were all within 8% of literature values. In addition, the thermal
conductivity of bulk SiO2 was measured as a function of temperature in a Helium-
4 cryostat from 75K to 250K. The results showed good agreement with literature
values that all fell within the error range of each measurement. The uncertainty in
the measurements ranged from 19% at 75K to 30% at 250K. Finally, the system
was used to measure the room temperature thermal conductivity of a nanocomposite
composed of cadmium selenide, CdSe, nanocrystals in an indium selenide, In2Se3,
matrix as a function of the concentration of In2Se3. The observed trend was in
qualitative agreement with the expected behavior.
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