Obstacle Avoidance of a Mobile Robot Using Fuzzy Logic Control

During the past several years fuzzy logic control (FLC) has emerged as one of the most active and fiuitful areas for research in the application of intelligent system design. Presently, fuzzy logic has found a variety of applications in various fields ranging from industrial process control to me...

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Main Author:	Eshtawie, Mohamed Al-Mahdi
Format:	Thesis
Language:	English English
Published:	1999
Subjects:	Mobile robota - Fuzzy logic - Case studies
Online Access:	http://psasir.upm.edu.my/id/eprint/10243/1/FK_1999_6.pdf
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id	my-upm-ir.10243
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spelling	my-upm-ir.102432024-03-25T03:24:43Z Obstacle Avoidance of a Mobile Robot Using Fuzzy Logic Control 1999-11 Eshtawie, Mohamed Al-Mahdi During the past several years fuzzy logic control (FLC) has emerged as one of the most active and fiuitful areas for research in the application of intelligent system design. Presently, fuzzy logic has found a variety of applications in various fields ranging from industrial process control to medical diagnosis and securities trading. Most notably, a fuzzy logic system has been applied to control nonlinear, time-varying, ill-defined systems, to control systems whose dynamics are not exactly known, as servomotors position control, and robot arm control, and to manage complex decision-making or diagnostic systems. This project has the objective of designing a fuzzy logic controller, which will be used to control the navigation process of an autonomous mobile robot in a completely unstructured environment. The navigation algorithm is proposed for static obstacles and with no priori knowledge about the environment. In addition, an on-line path planning is used while navigation. The controller will have its inputs from the sensors that will be mounted on the robot. The number of sensors used is five where, three of them will be on the front side of the robot, whereas, one on the left side and one on the right side. The FLC was designed using three different fuzzifiers (triangular, trapezoidal and Gaussian) to represent the sensor readings values so that they can be interpreted by the inference mechanism. Moreover, two different implication methods (Mamdani minimum and Mamdani Product) implications are used in the interpretation of the IF-THEN rules in the rule-base. Depending on the number of fuzzy sets used to represent the sensor readings, the total number of control rules used in the design was 243 at the first stage and then reduced to 1 08. In other words, if the number of fuzzy sets used to represent each sensor reading is three (far, near, and very near) then the total number of rules is 243 which is (35). On the other hand, if the left and right sensors reading values were represented using only two fuzzy sets (far and near) then the total number of rules is 1 08 i.e. (33 *22 ). In addition, two defuzzification strategies (center of gravity and center average) were used to get the output of the FLC in a crisp value. It was observed that the triangular fuzzifier, center average defuzzification method, and the Mamdani minimum implication method with a total number of 108 rule are the best choices for the design. Mobile robota - Fuzzy logic - Case studies 1999-11 Thesis http://psasir.upm.edu.my/id/eprint/10243/ http://psasir.upm.edu.my/id/eprint/10243/1/FK_1999_6.pdf text en public masters Universiti Putra Malaysia Mobile robota - Fuzzy logic - Case studies Faculty of Engineering Hasan, Md. Mahmud English
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English English
advisor	Hasan, Md. Mahmud
topic	Mobile robota - Fuzzy logic - Case studies
spellingShingle	Mobile robota - Fuzzy logic - Case studies Eshtawie, Mohamed Al-Mahdi Obstacle Avoidance of a Mobile Robot Using Fuzzy Logic Control
description	During the past several years fuzzy logic control (FLC) has emerged as one of the most active and fiuitful areas for research in the application of intelligent system design. Presently, fuzzy logic has found a variety of applications in various fields ranging from industrial process control to medical diagnosis and securities trading. Most notably, a fuzzy logic system has been applied to control nonlinear, time-varying, ill-defined systems, to control systems whose dynamics are not exactly known, as servomotors position control, and robot arm control, and to manage complex decision-making or diagnostic systems. This project has the objective of designing a fuzzy logic controller, which will be used to control the navigation process of an autonomous mobile robot in a completely unstructured environment. The navigation algorithm is proposed for static obstacles and with no priori knowledge about the environment. In addition, an on-line path planning is used while navigation. The controller will have its inputs from the sensors that will be mounted on the robot. The number of sensors used is five where, three of them will be on the front side of the robot, whereas, one on the left side and one on the right side. The FLC was designed using three different fuzzifiers (triangular, trapezoidal and Gaussian) to represent the sensor readings values so that they can be interpreted by the inference mechanism. Moreover, two different implication methods (Mamdani minimum and Mamdani Product) implications are used in the interpretation of the IF-THEN rules in the rule-base. Depending on the number of fuzzy sets used to represent the sensor readings, the total number of control rules used in the design was 243 at the first stage and then reduced to 1 08. In other words, if the number of fuzzy sets used to represent each sensor reading is three (far, near, and very near) then the total number of rules is 243 which is (35). On the other hand, if the left and right sensors reading values were represented using only two fuzzy sets (far and near) then the total number of rules is 1 08 i.e. (33 *22 ). In addition, two defuzzification strategies (center of gravity and center average) were used to get the output of the FLC in a crisp value. It was observed that the triangular fuzzifier, center average defuzzification method, and the Mamdani minimum implication method with a total number of 108 rule are the best choices for the design.
format	Thesis
qualification_level	Master's degree
author	Eshtawie, Mohamed Al-Mahdi
author_facet	Eshtawie, Mohamed Al-Mahdi
author_sort	Eshtawie, Mohamed Al-Mahdi
title	Obstacle Avoidance of a Mobile Robot Using Fuzzy Logic Control
title_short	Obstacle Avoidance of a Mobile Robot Using Fuzzy Logic Control
title_full	Obstacle Avoidance of a Mobile Robot Using Fuzzy Logic Control
title_fullStr	Obstacle Avoidance of a Mobile Robot Using Fuzzy Logic Control
title_full_unstemmed	Obstacle Avoidance of a Mobile Robot Using Fuzzy Logic Control
title_sort	obstacle avoidance of a mobile robot using fuzzy logic control
granting_institution	Universiti Putra Malaysia
granting_department	Faculty of Engineering
publishDate	1999
url	http://psasir.upm.edu.my/id/eprint/10243/1/FK_1999_6.pdf
_version_	1804888530116149248

Obstacle Avoidance of a Mobile Robot Using Fuzzy Logic Control

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