Term of Award

Summer 2022

Degree Name

Master of Science, Electrical Engineering

Document Type and Release Option

Thesis (restricted to Georgia Southern)

Copyright Statement / License for Reuse

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


Department of Electrical and Computer Engineering

Committee Chair

Fernando Rios

Committee Member 1

Rocio Alba-Flores

Committee Member 2

Mohammad Ahad


The recent years have seen the growth of robots being commercialized for their ability to complete basic tasks, and as the demands for robots increase, so do the difficulty in their tasks. Thus, we look to swarm robotics to supply the demand of robots without increasing the complexity on their design. Through swarm intelligence, a task can be broken down into simpler tasks that can be achievable by simple robots. A large number of these simple robots should be able to complete the task through repetition. This work focused on the simulation of an algorithm that allowed the robots to surround a shape by its perimeter. The waypoints that the robots must reach will be defined by the results of a Quadratic Assignment Problem (QAP) and the swarms pathing behavior will be defined by Ant Colony Optimization (ACO). The results from said simulation were then compared to those of an equal QAP were the Genetic algorithm (GA) was used for navigation. Within the ACO, the concept of stigmergy was implemented and observed. Scenarios where there are less than the ideal number of robots were also studied to show performance when facing robot failure. The simulation showed a successful completion of the task from both navigation algorithms, where the GA algorithm outperformed the ACO in terms of average iterations per task, yet unlike the ACO, the GA was not able to consistently reach the optimal solution for every scenario. To create such simulation, the software MATLAB was utilized.

OCLC Number


Research Data and Supplementary Material