Fuzzy Logic Based Control for Autonomous Mobile Robot Navigation

Omrane, Hajer; Masmoudi, Mohamed

doi:10.1155/2016/9548482

Cited by 110 publications

(64 citation statements)

References 6 publications

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“…In addition to this, fuzzy algorithms were used for the robot's path planning. In [13], a fuzzy logic controller was used for navigation and obstacle avoidance. In this study, the used mobile robot is equipped with eight infrared range sensors and the performances of intelligent navigation algorithm have been evaluated.…”

Section: Related Workmentioning

confidence: 99%

Collision-Free Mobile Robot navigation using Fuzzy Logic Approach

Dirik¹

2018

IJCA

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Autonomous mobile robots' navigation has become a very popular and interesting topic of computer science and robotics in the last decade. Many algorithms have been developed for robot motion control in an unknown (indoor/outdoor) and in various environments (static/dynamic). Fuzzy logic control techniques are an important algorithm developed for robot navigation problems. The aim of this research is to design and develop a fuzzy logic controller that enables the mobile robot to navigate to a target in an unknown environment, using WEBOTS commercial mobile robot simulation and MATLAB software. The algorithm is divided into two stages; In the first stage, the mobile robot was made to go to the goal, and in the second stage, obstacle avoidance was realized. Robot position information (x, y, Ø) was used to move the robot to the target and six sensors data were used during the obstacle avoidance phase. The used mobile robot (E_PUCK) is equipped with 12 IR sensors to measure the distance to the obstacles. The fuzzy control system is composed of six inputs grouped in doubles which are left, front and right distance sensors two outputs which are the mobile robot's left and right wheel speeds. To check the simulation result for proposed methodology, WEBOTS simulator and MATLAB software were used. To modeling the environment in different complexity and design, this simulator was used. The experimental results have shown that the proposed architecture provides an efficient and flexible solution for autonomous mobile robots and the objective of this research has been successfully achieved. This research also indicated that WEBOT and MATLAB are suitable tools that could be used to develop and simulate mobile robot navigation system.

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Section: Related Workmentioning

confidence: 99%

Collision-Free Mobile Robot navigation using Fuzzy Logic Approach

Dirik¹

2018

IJCA

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“…Autonomous mobile robot implemented in indoor environment with fuzzy logic based control [10]. The autonomous mobile robot uses nine Infrared (IR) sensors to detect obstacle and avoid accident.…”

Section: Obstacle Avoidance Sensorsmentioning

confidence: 99%

Automated Mobile Robots – A Survey on Controls

Palaniswamy¹,

Alofi²,

Saeed³

et al. 2017

CAE

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This research mainly focuses on optimizing the transportation system in King Abdulaziz University (KAU) girls' campus by designing an autonomous robotic golf cart. The quality of any research would be promoted by conducting in-depth literature review in the domain. Hence, this research begins with a review to investigate similar artifacts (robotic cart and the ways of displaying the arrival time for the cart) that are relevant to the work. It is divided into six parts which are: autonomous mobile robots, the types of microcontrollers boards that are used in the robotic field, the types of alerts, the types of sensors that are used in autonomous mobile robot to move on track and avoid the object, the ways of displaying the arrival time schedule and the connection ways. The detailed survey on the above mentioned six areas are conducted and the results are discussed in terms of the techniques and the resources used, the methodology followed, the merits and demerits of the work, and the implementation details with the problems faced. Thus, the widely-used approaches suitable for the automated transportation system are studied and analyzed thoroughly in this literature, in order to provide directions for academicians and researchers for further work General TermsAutonomous, Robotics

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“…The main challenge of the autonomous navigation of mobile robots is the quickest possible and the most precise reaching of the target position. One of the most commonly used solution for local navigation, global navigation, path planning, steering control and rate control of a mobile robot is the fuzzy logic [2].…”

Section: Introductionmentioning

confidence: 99%

“…The fuzzy logic is increasingly adapted to problem-solving in the field of unstructured, dynamically changing environments, the description of which by mathematical equations is difficult [2]. Its biggest advantage is the fact, that this method is capable of controlling several variables simultaneously.…”

Section: Introductionmentioning

confidence: 99%

The effect of the number of inference rules of a fuzzy controller on the quality of control of a mobile robot

et al. 2019

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The application of intelligent control algorithms in the field of autonomous mobile robotics enables effective control of mobile robots with a minimal possible error. At present, most of commonly used systems to control an autonomous mobile robot are, however, too complicated to design. Our goal was to design a fuzzy controller with an optimal number of inference rules in a way to achieve the best possible level of quality of mobile robot control. The proposed controller was implemented in the mobile robot EN20, where the time of regulation and the absolute and the quadratic control surface were used to evaluate quality parameters. The analysis of the quality of control was performed with the use of a fuzzy controller with 9, 25 and 49 inference rules. We found from the results of modelling that the greatest influence on the quality of control of a mathematical model of the mobile robot had the number of inference rules of the fuzzy controller. Mathematical and graphical dependence of the quality of control on the number of inference rules was calculated from the parameters of the quality of control. The results of the research are equations of the curves of the individual parameters of control of quality, which show that for the control of the autonomous mobile robot EN 20, that the optimal fuzzy controller has 49 inference rules, triangular functions of the pertinence of individual linguistic values and it is defuzzificated by Centroid method.

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Fuzzy Logic Based Control for Autonomous Mobile Robot Navigation

Cited by 110 publications

References 6 publications

Collision-Free Mobile Robot navigation using Fuzzy Logic Approach

Collision-Free Mobile Robot navigation using Fuzzy Logic Approach

Automated Mobile Robots – A Survey on Controls

The effect of the number of inference rules of a fuzzy controller on the quality of control of a mobile robot

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