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Author ORCID Identifier

https://orcid.org/0000-0002-0651-7882

Document Type

Open Access Dissertation

Degree Name

Doctor of Philosophy (PhD)

Degree Program

Computer Science

Year Degree Awarded

2019

Month Degree Awarded

May

First Advisor

Roderic Grupen

Second Advisor

Shlomo Zilberstein

Third Advisor

Joydeep Biswas

Fourth Advisor

Frank C. Sup IV

Subject Categories

Artificial Intelligence and Robotics

Abstract

Autonomy in robot systems is a valuable attribute that remains an elusive goal. Noisy sensors, stochastic actions, and variation in unstructured environments all lead to unavoidable errors that can be inconsequential or catastrophic depending on the circumstances. Developing techniques capable of mitigating uncertainty at runtime has, therefore, been a significant and challenging focus of the robotics community.

The primary contribution of this dissertation is the introduction of a new hierarchical belief space planning architecture to manage uncertainty and solve tasks using a uniform framework. Such an approach provides a means of creating autonomous systems that focus on salient subsets of state information, mitigate risk, and require less frequent intervention. Results indicate that it is possible to implement near optimal solutions to interesting problems in a uniform, hierarchical framework of belief space planners by taking actions that condense belief towards goal distributions. Example hierarchies are presented to address simple assembly problems and to enable robust long-term autonomous mobile manipulation in deployments lasting on the order of hours during which the robot executes hundreds of actions.

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