Practical Design of a Path Following for a Non-holonomic Mobile Robot Based on a Decentralized Fuzzy Logic Controller and Multiple Cameras

2016 ◽  
Vol 41 (8) ◽  
pp. 3215-3229 ◽  
Author(s):  
Emad A. Elsheikh ◽  
M. A. El-Bardini ◽  
M. A. Fkirin
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Fuzzy Logic Controller ◽  
Path Following ◽  
Multiple Cameras ◽  
Practical Design ◽  
Holonomic Mobile Robot

Autonomous path following by fuzzy adaptive curvature-based point selection algorithm for four-wheel-steering car-like mobile robot

2017 ◽  
Vol 232 (15) ◽  
pp. 2655-2665 ◽  
Author(s):  
S Ghaffari ◽  
MR Homaeinezhad
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Path Following ◽  
Selection Algorithm ◽  
Point Selection ◽  
Tracking Point ◽  
Takagi Sugeno ◽  
Fuzzy Adaptive

Autonomous path following in mobile robots with nonholonomic constraints can be divided into two problems: first, selecting the tracking point, and then designing an appropriate controller to follow the selected point. When selecting tracking point, considering the kinematics of the robot as well as characteristics of the desired path is of considerable importance. For these purposes, a curvature-based point selection algorithm is first proposed for the car-like mobile robot with independent steering mechanisms. Each instant, the proposed algorithm finds a point which enables the robot to be tangent to the path at that specific point. Afterwards, in order to take into consideration characteristics of the path, a fuzzy adaptive curvature-based point selection algorithm is proposed. In this algorithm, in addition to the kinematic constraints, path characteristics are also considered in selecting the tracking point. This gives the robot the ability to show better performance when the path slope changes suddenly, resulting in less overshoot/undershoot around the desired path. The fuzzy adaptive curvature-based point selection algorithm is combined with a controller based on the Takagi–Sugeno fuzzy logic, such that the fuzzy adaptive curvature-based point selection algorithm selects the tracking point, while the Takagi–Sugeno fuzzy logic controller makes the robot follow the selected point. Finally, the fuzzy adaptive curvature-based point selection–Takagi–Sugeno fuzzy logic tracker is implemented on the robot, and the results are compared with a similar path-following algorithm. Obtained results show that for tracking a piecewise linear path, the steering activity and the following root mean square error decrease from 170.74° and 0.37 m for the conventional fuzzy controller to 63.37° and 0.09 m for the fuzzy adaptive curvature-based point selection–Takagi–Sugeno fuzzy logic controller, respectively.

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.

scholarly journals Mobile Robot Wall-Following Control Using Fuzzy Logic Controller with Improved Differential Search and Reinforcement Learning

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1254 ◽  
Author(s):  
Cheng-Hung Chen ◽  
Shiou-Yun Jeng ◽  
Cheng-Jian Lin
Keyword(s):  
Fuzzy Logic ◽  
Reinforcement Learning ◽  
Mobile Robot ◽  
Fuzzy Logic Controller ◽  
Search Algorithm ◽  
Experimental Results ◽  
Average Error ◽  
Stopover Site ◽  
Accumulated Reward ◽  
Wall Following

In this study, a fuzzy logic controller with the reinforcement improved differential search algorithm (FLC_R-IDS) is proposed for solving a mobile robot wall-following control problem. This study uses the reward and punishment mechanisms of reinforcement learning to train the mobile robot wall-following control. The proposed improved differential search algorithm uses parameter adaptation to adjust the control parameters. To improve the exploration of the algorithm, a change in the number of superorganisms is required as it involves a stopover site. This study uses reinforcement learning to guide the behavior of the robot. When the mobile robot satisfies three reward conditions, it gets reward +1. The accumulated reward value is used to evaluate the controller and to replace the next controller training. Experimental results show that, compared with the traditional differential search algorithm and the chaos differential search algorithm, the average error value of the proposed FLC_R-IDS in the three experimental environments is reduced by 12.44%, 22.54% and 25.98%, respectively. Final, the experimental results also show that the real mobile robot using the proposed method can effectively implement the wall-following control.

Fuzzy Logic Controller to control the position of a mobile robot that follows a track on the floor

2020 ◽  
Author(s):  
Mohammed Salah Abood ◽  
Isam Kareem Thajeel ◽  
Emad M. Alsaedi ◽  
Mustafa Maad Hamdi ◽  
Ahmed Shamil Mustafa ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Fuzzy Logic Controller
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