A Hybrid of Neural Networks and Genetic Algorithms for Controlling Mobile Robots
Abstract
Autonomous and semiautonomous robots are certain to play a major role in several areas in the future, from the battlefield to the household. Countless different methodologies have been applied to solve the problem of mobile robot navigation, with varying degrees of success. SAMUEL, a genetic algorithm based system for evolving semi-autonomous agent behaviors, has proven successful in generating the necessary rule sets for navigating a simple environment. Fuzzy AR TMAP (FAM) neural networks have also been applied in a similar fashion, again with success. In this thesis, a hybrid system is developed. The system fuses both SAMUEL and FAM neural networks, using SAMUEL to develop rule sets for which the FAM provides motion prediction information. The FAM motion predictor serves as an input to the genetic algorithm, so the genetic algorithm can utilize this capability without modification. A simulation using the hybrid system is developed and run, demonstrating how agents controlled by the system would respond to an example mission. The effectiveness of this approach is compared to SAMUEL's ability to complete this task unaided. Finally, open-source source code is made available.
Notes
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Thesis Completion
2004
Semester
Spring
Advisor
Schiavone, Guy
Degree
Bachelor of Science (B.S.)
College
College of Engineering and Computer Science
Degree Program
Computer Engineering
Subjects
Dissertations, Academic -- Engineering and Computer Science; Engineering -- Dissertations, Academic
Format
Identifier
DP0021859
Language
English
Access Status
Open Access
Length of Campus-only Access
None
Document Type
Honors in the Major Thesis
Recommended Citation
Secretan, James, "A Hybrid of Neural Networks and Genetic Algorithms for Controlling Mobile Robots" (2004). HIM 1990-2015. 399.
https://stars.library.ucf.edu/honorstheses1990-2015/399