Date of Award
2012
Publication Type
Master Thesis
Degree Name
M.A.Sc.
Department
Computer Science
Keywords
Artificial intelligence.
Supervisor
Wu, Dan (Computer Science)
Rights
info:eu-repo/semantics/openAccess
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Abstract
In this thesis, we tackle the problem of extending neural network navigation algorithms for various types of mobile robots and 2-dimensional range sensors. We propose a general method to interpret the data from various types of 2-dimensional range sensors and a neural network algorithm to perform the navigation task. Our approach can yield a global navigation algorithm which can be applied to various types of range sensors and mobile robot platforms. Moreover, this method allows the neural networks to be trained using only one type of 2-dimensional range sensor, which contributes positively to reducing the time required for training the networks. Experimental results carried out in simulation environments demonstrate the effectiveness of our approach in mobile robot navigation for different kinds of robots and sensors. Therefore, the successful implementation of our method provides a solution to apply mobile robot navigation algorithms to various robot platforms.
Recommended Citation
Dezfoulian, Seyyed Hamid, "A Generalized Neural Network Approach to Mobile Robot Navigation and Obstacle Avoidance" (2012). Electronic Theses and Dissertations. 102.
https://scholar.uwindsor.ca/etd/102