Study on ultrasonic obstacle avoidance of mobile robot based on fuzzy controller

2010 ◽  
Author(s):  
Shigang Cui ◽  
Xiong Su ◽  
Li Zhao ◽  
Zhigang Bing ◽  
Genghuang Yang
Keyword(s):  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Fuzzy Controller

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 Autonomous system to control a mobile robot

2020 ◽  
Vol 9 (4) ◽  
pp. 1711-1717
Author(s):  
Ayman Abu Baker ◽  
Yazeed Yasin Ghadi
Keyword(s):  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Autonomous Navigation ◽  
Fuzzy Controller ◽  
Computational Time ◽  
Ongoing Effort ◽  
Unstructured Environment ◽  
Adaptive Inference ◽  
Real Time Applications ◽  
Linguistic Labels

This paper presents an ongoing effort to control a mobile robot in unstructured environment. Obstacle avoidance is an important task in the field of robotics, since the goal of autonomous robot is to reach the destination without collision. Several algorithms have been proposed for obstacle avoidance, having drawbacks and benefits. In this paper, the fuzzy controller is used to tackle the problem of mobile robot autonomous navigation in unstructured environment. The objective is to make the robot move along a collision free trajectory until it reaches its target. The proposed approach uses the fuzzified, adaptive inference engine and defuzzification engine. Also number of linguistic labels is optimized for the input of the mobile robot in order to reduce computational time for real-time applications. The proposed fuzzy controller is evaluated subjectively and objectively with other approaches and also the processing time is taken in consideration.

scholarly journals Using Ultrasonic Sensors and a Knowledge-Based Neural Fuzzy Controller for Mobile Robot Navigation Control

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 466
Author(s):  
Cheng-Hung Chen ◽  
Cheng-Jian Lin ◽  
Shiou-Yun Jeng ◽  
Hsueh-Yi Lin ◽  
Cheng-Yi Yu
Keyword(s):  
Mobile Robot ◽  
Differential Evolution ◽  
Obstacle Avoidance ◽  
Fuzzy Controller ◽  
Robot Navigation ◽  
Mobile Robot Navigation ◽  
Knowledge Based ◽  
Automatic Navigation ◽  
Navigation Control ◽  
Neural Fuzzy

This study proposes a knowledge-based neural fuzzy controller (KNFC) for mobile robot navigation control. An effective knowledge-based cultural multi-strategy differential evolution (KCMDE) is used for adjusting the parameters of KNFC. The KNFC is applied in PIONEER 3-DX mobile robots to achieve automatic navigation and obstacle avoidance capabilities. A novel escape approach is proposed to enable robots to autonomously avoid special environments. The angle between the obstacle and robot is used and two thresholds are set to determine whether the robot entries into the special landmarks and to modify the robot behavior for avoiding dead ends. The experimental results show that the proposed KNFC based on the KCMDE algorithm has improved the learning ability and system performance by 15.59% and 79.01%, respectively, compared with the various differential evolution (DE) methods. Finally, the automatic navigation and obstacle avoidance capabilities of robots in unknown environments were verified for achieving the objective of mobile robot control.

Autonomous omnidirectional mobile robot navigation based on hierarchical fuzzy systems

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Najla Krichen ◽  
Mohamed Slim Masmoudi ◽  
Nabil Derbel
Keyword(s):  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Fuzzy Systems ◽  
Fuzzy Controller ◽  
Fuzzy Rules ◽  
Infrared Sensors ◽  
Content Type ◽  
Omnidirectional Mobile Robot ◽  
Simulation Results ◽  
Hierarchical Fuzzy Systems

Purpose This paper aims to propose a one-layer Mamdani hierarchical fuzzy system (HFS) to navigate autonomously an omnidirectional mobile robot to a target with a desired angle in unstructured environment. To avoid collision with unknown obstacles, Mamdani limpid hierarchical fuzzy systems (LHFS) are developed based on infrared sensors information and providing the appropriate linear speed controls. Design/methodology/approach The one-layer Mamdani HFS scheme consists of three fuzzy logic units corresponding to each degree of freedom of the holonomic mobile robot. This structure makes it possible to navigate with an optimized number of rules. Mamdani LHFS for obstacle avoidance consists of a number of fuzzy logic units of low dimension connected in a hierarchical structure. Hence, Mamdani LHFS has the advantage of optimizing the number of fuzzy rules compared to a standard fuzzy controller. Based on sensors information inputs of the Mamdani LHFS, appropriate linear speed controls are generated to avoid collision with static obstacles. Findings Simulation results are performed with MATLAB software in interaction with the environment test tool “Robotino Sim.” Experiments have been done on an omnidirectional mobile robot “Robotino.” Simulation results show that the proposed approaches lead to satisfied performances in navigation between static obstacles to reach the target with a desired angle and have the advantage that the total number of fuzzy rules is greatly reduced. Experimental results prove the efficiency and the validity of the proposed approaches for the navigation problem and obstacle avoidance collisions. Originality/value By comparing simulation results of the proposed Mamdani HFS to another navigational controller, it was found that it provides better results in terms of path length in the same environment. Moreover, it has the advantage that the number of fuzzy rules is greatly reduced compared to a standard Mamdani fuzzy controller. The use of Mamdani LHFS in obstacle avoidance greatly reduces the number of involved fuzzy rules and overcomes the complexity of high dimensionality of the infrared sensors data information.

Behavior-Based Fuzzy Control of Obstacle Avoidance for Indoor Mobile Robot

2010 ◽  
Vol 34-35 ◽  
pp. 482-486
Author(s):  
De Hai Chen ◽  
Yu Ming Liang
Keyword(s):  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Fuzzy Controller ◽  
Input Parameter ◽  
Middle Level ◽  
Infrared Range ◽  
Indoor Mobile Robot ◽  
Behavior Based ◽  
Task Oriented ◽  
And Task

This paper describes indoor mobile robot covering path to avoid obstacle based on behavior fuzzy controller. The robot measures the distance to obstacle with ultrasonic sensors and infrared range sensors, and the distance is the input parameter of the behavior-based fuzzy controller. The behavior architecture has three levels behaviors: emergency behavior, obstacle avoidance behavior, and task oriented behavior. The task oriented behavior is the highest level behavior, and has two subtasks: wall following and path covering. The middle level behavior is obstacle avoidance. The lowest level is an emergency behavior, which is the highest priority behavior. The simulation result demonstrates that each behavior works correctly.

Optimal fuzzy tracking control with obstacles avoidance for a mobile robot based on Takagi-Sugeno fuzzy model

2018 ◽  
Vol 41 (10) ◽  
pp. 2772-2781 ◽  
Author(s):  
Khaled Akka ◽  
Farid Khaber
Keyword(s):  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Tracking Control ◽  
Fuzzy Controller ◽  
Linear Quadratic Regulator ◽  
Avoidance Task ◽  
Reference Trajectory ◽  
Linear Quadratic ◽  
Trajectory Tracking Control ◽  
Takagi Sugeno

This paper presents a trajectory tracking control approach for a mobile robot in the presence of static obstacles on the reference trajectory. The tracking control is developed based on the fuzzy parallel distributed compensation scheme where the linear quadratic regulator is used as a state feedback controller for each subsystem. The obstacle avoidance task is accomplished by applying a linear quadratic regulator on the overall open loop Takagi-Sugeno (T-S) fuzzy system, in which the weighting matrices are adjusted dynamically using a fuzzy controller. A fuzzy fusion controller is designed to combine the tracking and the obstacle avoidance velocities in order to perform both tasks simultaneously. Simulation results illustrate the effectiveness of the proposed control strategy.

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.

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