Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Major/Program

Computer Science

First Advisor's Name

Leonardo Bobadilla

First Advisor's Committee Title

Committee chair

Second Advisor's Name

Mark Finlayson

Second Advisor's Committee Title

Committee member

Third Advisor's Name

Monique Ross

Third Advisor's Committee Title

Committee member

Fourth Advisor's Name

Ning Xie

Fourth Advisor's Committee Title

Committee member

Fifth Advisor's Name

Dwayne McDaniel

Fifth Advisor's Committee Title

Committee member

Keywords

robotics, motion planning, human-robot-interaction, scheduling, coordination, path-planning, decommissioning, inspection

Date of Defense

11-2-2018

Abstract

Large quantities of high-level radioactive waste were generated during WWII. This waste is being stored in facilities such as double-shell tanks in Washington, and the Waste Isolation Pilot Plant in New Mexico. Due to the dangerous nature of radioactive waste, these facilities must undergo periodic inspections to ensure that leaks are detected quickly. In this work, we provide a set of methodologies to aid in the monitoring and inspection of these hazardous facilities. This allows inspection of dangerous regions without a human operator, and for the inspection of locations where a person would not be physically able to enter.

First, we describe a robot equipped with sensors which uses a modified A* path-planning algorithm to navigate in a complex environment with a tether constraint. This is then augmented with an adaptive informative path planning approach that uses the assimilated sensor data within a Gaussian Process distribution model. The model's predictive outputs are used to adaptively plan the robot's path, to quickly map and localize areas from an unknown field of interest. The work was validated in extensive simulation testing and early hardware tests.

Next, we focused on how to assign tasks to a heterogeneous set of robots. Task assignment is done in a manner which allows for task-robot dependencies, prioritization of tasks, collision checking, and more realistic travel estimates among other improvements from the state-of-the-art. Simulation testing of this work shows an increase in the number of tasks which are completed ahead of a deadline.

Finally, we consider the case where robots are not able to complete planned tasks fully autonomously and require operator assistance during parts of their planned trajectory. We present a sampling-based methodology for allocating operator attention across multiple robots, or across different parts of a more sophisticated robot. This allows few operators to oversee large numbers of robots, allowing for a more scalable robotic infrastructure. This work was tested in simulation for both multi-robot deployment, and high degree-of-freedom robots, and was also tested in multi-robot hardware deployments.

The work here can allow robots to carry out complex tasks, autonomously or with operator assistance. Altogether, these three components provide a comprehensive approach towards robotic deployment within the deactivation and decommissioning tasks faced by the Department of Energy.

Identifier

FIDC007010

Available for download on Friday, October 16, 2020

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