Identification and characterization of a target object included within a surrounding medium is of interest in a variety of fields ranging from medical imaging and diagnostics, to counter-mine operations in military environments. The work described in this document applies a new approach to this problem through the development of Dynamic Interrogation, whereby physical stimuli are combined with imaging techniques to identify physical properties of a target inclusion.
Interaction with physical systems in the real world brings with it a number of challenges, namely the stochastic nature of physical systems which adds complexity and uncertainty to any analysis or manipulation performed. In the field of optimal control, stochastic systems are well-studied. However, most approaches from literature seek to minimize the mean cost value of the system. An alternative approach found in literature is to minimize the variance of the cost function while constraining the mean cost to some selected value. This approach is known as Minimal Cost Variance control, and has been demonstrated to regulate finite continuous-time systems while minimizing the cost variance. In this work, we expand this technique to develop a new optimal control methodology suitable for robotic tracking applications.
The unknown nature of the interrogation target provides an opportunity to leverage techniques from Model Predictive Control approaches to further improve the performance of the Dynamic Interrogation system. As a result, the developed novel control focuses on utilizing a minimal cost variance approach within the framework of model predictive control to optimize the performance of a Dynamic Interrogation system and facilitate implementation in an online architecture.
To do this, we first develop the solution to the continuous-time, full-state feedback tracking MCV control problem which enables stochastic systems track a-priori state trajectories while minimizing the cost variance of the system. Next, we extend the tracking MCV solution to the discrete-time case to facilitate implementation in online architectures and to integrate with architectures from the field of model predictive control, most of which are implemented in discrete time. Finally, we derive the full Model Predictive Minimal Cost Variance (MPMCV) control and provide example implementations to physical systems.
In addition to the optimal control work, this dissertation includes the development and structure of Dynamic Interrogation as implemented by robotic systems. The initial application for development of a Dynamic Interrogation approach focuses on tumor detection and characterization within biological tissue by implementation of recently developed imaging techniques to robotic manipulation utilizing novel optimal control approaches. This dissertation presents a nominal architecture for implementation of Dynamic Interrogation on the Baxter research robot, a platform developed specifically for applications in close proximity to humans. Additionally, the development of a Tactile Imagining sensor designed for integration with the robotic platform is presented. The integrated sensor system was then installed and tested on the Baxter robot within a laboratory environment, and experimentation done to verify the abilities and performance of the full system on phantom tissue analogs. The work concludes with experimental results from this implementation and a discussion of future work. / Electrical and Computer Engineering
| Identifer | oai:union.ndltd.org:TEMPLE/oai:scholarshare.temple.edu:20.500.12613/9517 |
| Date | 12 1900 |
| Creators | Lash, Stephen, 0009-0001-2975-7707 |
| Contributors | Won, Chang-Hee, 1967-, Bai, Li, Biswas, Saroj, Priess, Cody |
| Publisher | Temple University. Libraries |
| Source Sets | Temple University |
| Language | English |
| Detected Language | English |
| Type | Thesis/Dissertation, Text |
| Format | 255 pages |
| Rights | IN COPYRIGHT- This Rights Statement can be used for an Item that is in copyright. Using this statement implies that the organization making this Item available has determined that the Item is in copyright and either is the rights-holder, has obtained permission from the rights-holder(s) to make their Work(s) available, or makes the Item available under an exception or limitation to copyright (including Fair Use) that entitles it to make the Item available., http://rightsstatements.org/vocab/InC/1.0/ |
| Relation | http://dx.doi.org/10.34944/dspace/9479, Theses and Dissertations |
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