scholarly journals Fuzzy Logic Deadzone Compensation for a Mobile Robot

10.5772/13676 ◽  
2011 ◽  
Author(s):  
Jun Oh
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot

Design and FPGA Implementation of a New Intelligent Behaviors Fusion for Mobile Robot Using Fuzzy Logic

2019 ◽  
Vol 12 (1) ◽  
pp. 1 ◽  
Author(s):  
Badr El Kari ◽  
Hassan Ayad ◽  
Abdeljalil El Kari ◽  
Mostafa Mjahed ◽  
Claudiu Pozna
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Fpga Implementation

Fuzzy Logic Controller for Obstacle Avoidance of Mobile Robot

2019 ◽  
Vol 20 (1) ◽  
pp. 51-62
Author(s):  
Rajmeet Singh ◽  
Tarun Kumar Bera
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Dynamic Model ◽  
Obstacle Avoidance ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Bond Graph ◽  
Membership Functions ◽  
Current Position ◽  
Dc Motors

AbstractThis work describes design and implementation of a navigation and obstacle avoidance controller using fuzzy logic for four-wheel mobile robot. The main contribution of this paper can be summarized in the fact that single fuzzy logic controller can be used for navigation as well as obstacle avoidance (static, dynamic and both) for dynamic model of four-wheel mobile robot. The bond graph is used to develop the dynamic model of mobile robot and then it is converted into SIMULINK block by using ‘S-function’ directly from SYMBOLS Shakti bond graph software library. The four-wheel mobile robot used in this work is equipped with DC motors, three ultrasonic sensors to measure the distance from the obstacles and optical encoders to provide the current position and speed. The three input membership functions (distance from target, angle and distance from obstacles) and two output membership functions (left wheel voltage and right wheel voltage) are considered in fuzzy logic controller. One hundred and sixty-two sets of rules are considered for motion control of the mobile robot. The different case studies are considered and are simulated using MATLAB-SIMULINK software platform to evaluate the performance of the controller. Simulation results show the performances of the navigation and obstacle avoidance fuzzy controller in terms of minimum travelled path for various cases.

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