Date of Award
2009-01-01
Degree Name
Master of Science
Department
Electrical Engineering
Advisor(s)
Patricia Nava
Abstract
Today is an era in which humans need the help of automated machines to facilitate their hectic lives. Over the past few decades, robotics has been one of the most researched areas in the world. One area of this research is obstacle avoidance for autonomous vehicles. In order to avoid an obstacle, the system may include two important characteristics: obstacle detection and avoidance control. In this Thesis a classical logic system and artificial neural network approach is presented. With the help of an Integrated Development Environment, a virtual robot was able to negotiate a maze and avoid obstacles according to the data gathered from its sensors. The latter approach was compared to an Artificial Neural Network (ANN) configuration, which proved to perform with successful results. This Thesis demonstrates that the use of classical logic systems and ANN offers a good solution to the problem of obstacle-avoidance while negotiating a maze environment. ANN can be considered to be faster, once the neural network is trained, in response to obstacle-avoidance due to its massive parallel processing.
Language
en
Provenance
Received from ProQuest
Copyright Date
2009
File Size
92 pages
File Format
application/pdf
Rights Holder
Javier Alejandro Flores
Recommended Citation
Flores, Javier Alejandro, "Autonomous Vehicle Navigation: A Comparative Study Of Classical Logic And Neural Network Technique" (2009). Open Access Theses & Dissertations. 257.
https://scholarworks.utep.edu/open_etd/257