Zachariah Fuchs (Advisor), John Gallagher (Committee Member), Luther Palmer (Committee Member)
Master of Science in Electrical Engineering (MSEE)
The purpose of this thesis is to examine the ability of evolutionary algorithms (EAs) to develop near optimal solutions to three different path planning control problems. First, we begin by examining the evolution of an open-loop controller for the turn-circle intercept problem. We then extend the evolutionary methodology to develop a solution to the closedloop Dubins Vehicle problem. Finally, we attempt to evolve a closed-loop solution to the turn constrained pursuit evasion problem. For each of the presented problems, a custom controller representation is used. The goal of using custom controller representations (as opposed to more standard techniques such as neural networks) is to show that simple representations can be very effective if problem specific knowledge is used. All of the custom controller representations described in this thesis can be easily implemented in any modern programming language without any extra toolboxes or libraries. A standard EA is used to evolve populations of these custom controllers in an attempt to generate near optimal solutions. The evolutionary framework as well as the process of mixing and mutation is described in detail for each of the custom controller representations. In the problems where an analytically optimal solution exists, the resulting evolved controllers are compared to the known optimal solutions so that we can quantify the EA's performance. A breakdown of the evolution as well as plots of the resulting evolved trajectories are shown for each of the analyzed problems.
Department or Program
Department of Electrical Engineering
Year Degree Awarded
Copyright 2018, some rights reserved. My ETD may be copied and distributed only for non-commercial purposes and may not be modified. All use must give me credit as the original author.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.