Award Date

5-1-2021

Degree Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Computer Science

First Committee Member

Evangelos Yfantis

Second Committee Member

Andreas Stefik

Third Committee Member

Hal Berghel

Fourth Committee Member

William Culbreth

Number of Pages

60

Abstract

The challenges of drone navigation have driven many advances in the development of autonomous systems. Unmanned Autonomous Vehicles(UAVs) operate in a rapidly changing flight space and have to balance a complex set of constraints and objectives. Many of these objectives can be represented in variations of the classic Traveling Salesman Problem. Numerous approximate solutions to TSP have been proposed over the years, but these approaches have difficulty when adding new constraints that require rapid recalculation of the solution. Either they are fast but do not provide solutions that are close to the optimum, or they provide excellent solutions but they take a large number of computational resources to arrive at a solution. We are proposing a new algorithm that will be able to provide a very competitive solution to TSP compared to other probabilistic and deterministic approaches. We will demonstrate that this new algorithm is robust and efficient enough to be effective within the strict computational constraints of a typical UAV avionics system.

Keywords

Genetic Algorithms; Prim's Algorithm; Simulated Annealing; Traveling Salesman Problem; Unmanned Autonomous Vehicles

Disciplines

Computer Sciences

File Format

pdf

File Size

804 KB

Degree Grantor

University of Nevada, Las Vegas

Language

English

Rights

IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/

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