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

This item is only available in print in the UCF Libraries. If this is your thesis or dissertation, you can help us make it available online for use by researchers around the world by downloading and filling out the Internet Distribution Consent Agreement. You may also contact the project coordinator Kerri Bottorff for more information.

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

Print

Identifier

DP0021859

Language

English

Access Status

Open Access

Length of Campus-only Access

None

Document Type

Honors in the Major Thesis

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