A Comparison of Fuzzy Logic Controller and PID Controller for Differential Drive Wall-Following Mobile Robot

2019 ◽  
Author(s):  
R.M.N.B. Ratnayake ◽  
T.S. de Silva ◽  
C.J. Rodrigo
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Differential Drive ◽  
Wall Following

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.

scholarly journals Implementation of Fuzzy Logic Controller for Wall Following and Obstacle Avoiding Robot

2019 ◽  
Vol 4 (1) ◽  
pp. 9-21
Author(s):  
Aryuanto Soetedjo ◽  
M. Ibrahim Ashari ◽  
Cosnas Eric Septian
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Fuzzy Logic Controller ◽  
Low Cost ◽  
Experimental Results ◽  
Raspberry Pi ◽  
Ultrasonic Sensors ◽  
Obstacle Avoiding ◽  
Wall Following

This paper presents the development of wall following and obstacle avoiding robot using a Fuzzy Logic Controller. The ultrasonic sensors are employed to measure the distances between robot and the wall, and between the robot and the obstacle. A low cost Raspberry Pi camera is employed to measure the left/right distance between the robot and the obstacle. The Fuzzy Logic Controller is employed to steer the mobile robot to follow the wall and avoid the obstacle according to the multi sensor inputs. The outputs of Fuzzy Logic Controller are the speeds of left motor and right motor. The experimental results show that the developed mobile robot could be controlled properly to follow the different wall positions and avoid the different obstacle positions with the high successful rate of 83.33%.

Fuzzy Logic Controller for Bidirectional Garaging of a Differential Drive Mobile Robot

2010 ◽  
Vol 24 (8-9) ◽  
pp. 1291-1311 ◽  
Author(s):  
Srđan T. Mitrović ◽  
Željko M. Ðurović
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Fuzzy Logic Controller ◽  
Differential Drive

scholarly journals Optimization of Fuzzy Logic Controller Used for a Differential Drive Wheeled Mobile Robot

2021 ◽  
Vol 11 (13) ◽  
pp. 6023
Author(s):  
Alexandr Štefek ◽  
Van Thuan Pham ◽  
Vaclav Krivanek ◽  
Khac Lam Pham
Keyword(s):  
Fuzzy Logic ◽  
Energy Consumption ◽  
Mobile Robot ◽  
Motion Control ◽  
Fuzzy Logic Controller ◽  
Operation Time ◽  
Wheeled Mobile Robot ◽  
Optimization Process ◽  
Differential Drive ◽  
Full Optimization

The energy-efficient motion control of a mobile robot fueled by batteries is an especially important and difficult problem, which needs to be continually addressed in order to prolong the robot’s independent operation time. Thus, in this article, a full optimization process for a fuzzy logic controller (FLC) is proposed. The optimization process employs a genetic algorithm (GA) to minimize the energy consumption of a differential drive wheeled mobile robot (DDWMR) and still ensure its other performances of the motion control. The earlier approaches mainly focused on energy reduction by planning the shortest path whereas this approach aims to optimize the controller for minimizing acceleration of the robot during point-to-point movement and thus minimize the energy consumption. The proposed optimized controller is based on fuzzy logic systems. At first, an FLC has been designed based on the experiment and as well as an experience to navigate the DDWMR to a known destination by following the given path. Next, a full optimization process by using the GA is operated to automatically generate the best parameters of all membership functions for the FLC. To evaluate its effectiveness, a set of other well-known controllers have been implemented in Google Colab® and Jupyter platforms in Python language to compare them with each other. The simulation results have shown that about 110% reduction of the energy consumption was achieved using the proposed method compared to the best of six alternative controllers. Also, this simulation program has been published as an open-source code for all readers who want to continue in the research.

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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