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 The Mobile Robot Control in Obstacle Avoidance Using Fuzzy Logic Controller

2020 ◽  
Vol 5 (3) ◽  
pp. 334-351
Author(s):  
M. Khairudin ◽  
R. Refalda ◽  
S. Yatmono ◽  
H. S. Pramono ◽  
A. K. Triatmaja ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Obstacle Avoidance ◽  
Experimental Work ◽  
Fuzzy Logic Controller ◽  
Input Voltage ◽  
Robot System ◽  
Dc Motors ◽  
Robot Systems

A very challenging problem in mobile robot systems is mostly in obstacle avoidance strategies. This study aims to describe how the obstacle avoidance system on mobile robots works. This system is designed automatically using fuzzy logic control (FLC) developed using Matlab to help the mobile robots to avoid a head-on collision. The FLC designs were simulated on the mobile robot system. The simulation was conducted by comparing FLC performance to the proportional integral derivative (PID) controller. The simulation results indicate that FLC works better with lower settling time performance. To validate the results, FLC was used in a mobile robot system. It shows that FLC can control the velocity by braking or accelerating according to the sensor input installed in front of the mobile robot. The FLC control system functions as ultrasonic sensor input or a distance sensor. The input voltage was simulated with the potentiometer, whereas the output was shown by the velocity of DC motor. This study employed the simulation work in Simulink and Matlab, while the experimental work used laboratory scale of mobile robots. The results show that the velocity control of DC motors with FLC produces accurate data, so the robot could avoid the existing obstacles. The findings indicate that the simulation and the experimental work of FLC for mobile robot in obstacle avoidance are very close.

scholarly journals Fuzzy Logic Controller sebagai Penentu Gerak Mobile Robot Pembasmi Hama

2020 ◽  
Vol 1 (01) ◽  
pp. 19-24
Author(s):  
Muhammad Ridho Kenawas ◽  
Pola Risma ◽  
Tresna Dewi ◽  
Selamet Muslimin ◽  
Yurni Oktarina
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
High Speed ◽  
Fuzzy Logic Controller ◽  
Pulse Width Modulation ◽  
Robot Motion ◽  
Crop Failure ◽  
Driving System ◽  
Dc Motors ◽  
Autonomous Decision

A mobile robot is one of the solutions to overcome crop failure caused by chili pests. The mobile robot discussed in this paper is used to spray pesticide liquid into chili plant stems to prevent pests attack on the plants. This paper discusses the design of pesticide spraying robot motion with the application of Fuzzy Logic Controller. This robot employment is expected to reduce farmers' workload and to help to produce a good harvest.  Robot motions are divided into two conditions, which can be controlled by remote control as a controller (manual) and by means of a sensor (automatic). Mobile robot movements have a significant impact on navigation and the design of the driving system. Robot speed is controller by adjusting Pulse Width Modulation of DC motors attached to the robots' wheel, which set to be  90 for slow and 220  for high speed. The Fuzzy Logic Controller in this mobile robot functions as an autonomous decision-making driver to detect obstacles in front of the mobile robot and the targeted stems.

scholarly journals Advanced Fuzzy Potential Field Method for Mobile Robot Obstacle Avoidance

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Jong-Wook Park ◽  
Hwan-Joo Kwak ◽  
Young-Chang Kang ◽  
Dong W. Kim
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Fuzzy Controller ◽  
Field Method ◽  
Linguistic Variables ◽  
Control Logic ◽  
Potential Field Method ◽  
Repulsive Forces

An advanced fuzzy potential field method for mobile robot obstacle avoidance is proposed. The potential field method primarily deals with the repulsive forces surrounding obstacles, while fuzzy control logic focuses on fuzzy rules that handle linguistic variables and describe the knowledge of experts. The design of a fuzzy controller—advanced fuzzy potential field method (AFPFM)—that models and enhances the conventional potential field method is proposed and discussed. This study also examines the rule-explosion problem of conventional fuzzy logic and assesses the performance of our proposed AFPFM through simulations carried out using a mobile robot.

scholarly journals Design of Fuzzy Membership Functions for Predicting Student’s Knowledge Performance

2019 ◽  
Vol 3 (6) ◽  
Author(s):  
Salisu Muhammad Sani
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Problem Solving ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Fuzzy Membership ◽  
Linguistic Variables ◽  
Membership Functions ◽  
Fuzzy Membership Functions

A Fuzzy logic controller is a problem-solving control system that provides means for representing approximate knowledge. The output of a fuzzy controller is derived from the fuzzifications of crisp (numerical) inputs using associated membership functions. The crisp inputs are usually converted to the different members of the associated linguistic variables based on their respective values. This point is evident enough to show that the output of a fuzzy logic controller is heavily dependent on its memberships of the different membership functions, which can be considered as a range of inputs [4]. Input membership functions can take various forms trapezoids, triangles, bell curves, singleton or any other shape that accurately enables the distribution of information within the system, in as much as the shape provides a region of transition between adjacent membership functions.

scholarly journals Robot navigation with obstacle avoidance in unknown environment

2018 ◽  
Vol 7 (4) ◽  
pp. 2410 ◽  
Author(s):  
Neerendra Kumar ◽  
Zoltán Vámossy
Keyword(s):  
Fuzzy Logic ◽  
Obstacle Avoidance ◽  
Fuzzy Inference System ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Robot Navigation ◽  
Training Data ◽  
Inference System ◽  
Pure Pursuit

In this paper, a robot navigation model is constructed in MATLAB-Simulink. This robot navigation model make the robot capable for the obstacles avoidance in unknown environment. The navigation model uses two types of controllers: pure pursuit controller and fuzzy logic controller. The role of the pure pursuit controller is to generate linear and angular velocities to drive the robot from its current position to the given goal position. The obstacle avoidance is achieved through the fuzzy logic controller. For the fuzzy controller, two novel fuzzy inference systems (FISs) are developed. Initially, a Mamdani-type fuzzy inference system (FIS) is generated. Using this Mamdani-type FIS in the fuzzy controller, the training data of input and output mapping, is collected. This training data is supplied to the adaptive neuro-fuzzy inference system (ANFIS) to obtain the second FIS as of Sugeno-type. The navigation model, using the proposed FISs, is implemented on the simulated as well as real robots.

scholarly journals Obstacle Avoidance during Robot Navigation in Dynamic Environment using Fuzzy Controller

2019 ◽  
Vol 8 (2) ◽  
pp. 817-822
Keyword(s):  
Fuzzy Logic ◽  
Angular Velocity ◽  
Obstacle Avoidance ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Robot Navigation ◽  
Dynamic Environment ◽  
Goal Location ◽  
Navigation Path ◽  
Pure Pursuit

A Simulink model containing fuzzy logic controller for collision-free robot navigation in a dynamic environment is presented in this paper. Two controllers, pure pursuit and fuzzy logic controller, are considered to handle robot navigation with obstacle avoidance. Ignoring the obstacles, the pure pursuit controller computes the required linear and angular velocities to direct robot from start to goal location. However, if obstacles are present in the navigation path then the robot will get collided with obstacles in the path. As a result, the robot will not reach to the provided goal location. The fuzzy logic controller is used to avoid obstacles in the navigation path. The fuzzy logic controller takes obstacle distance, obstacle angle, target direction and the x coordinate of goal location as inputs. Consequently, the fuzzy logic controller outputs the required change in angular velocity for the robot. This change in angular velocity is applied to the angular velocity provided by the pure pursuit controller. The experimental work is performed using Turtlebot Gazebo simulator. The navigation including environment, obstacles and resultant paths are also manifested.

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