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

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

Mathematics ◽

10.3390/math8081254 ◽

2020 ◽

Vol 8 (8) ◽

pp. 1254 ◽

Cited By ~ 1

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.

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