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 Mobile robot controller using novel hybrid system

2020 ◽  
Vol 10 (1) ◽  
pp. 1027
Author(s):  
Ayman A Abu Baker ◽  
Yazeed Ghadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mobile Robot ◽  
Autonomous Navigation ◽  
Processing Time ◽  
Fuzzy Controller ◽  
Robot Controller ◽  
Neuro Fuzzy ◽  
Unstructured Environment ◽  
Adaptive Inference

Hybrid neuro-fuzzy controller is one of the techniques that is used as a tool to control a mobile robot in unstructured environment. In this paper a novel neuro-fuzzy technique is proposed in order to tackle the problem of mobile robot autonomous navigation 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. The objective is to make the robot move along a collision free trajectory until it reaches its target. The proposed approach uses the artificial neural network instead of the fuzzified engine then the output from it is processed using adaptive inference engine and defuzzification engine. In this approach, the real processing time is reduce that is increase the mobile robot response. The proposed neuro-fuzzy controller is evaluated subjectively and objectively with other approaches and also the processing time is taken in consideration.

Obstacle Avoidance and Navigation of Autonomous Mobile Robot

2011 ◽  
Vol 403-408 ◽  
pp. 4633-4642 ◽  
Author(s):  
Rekha Raja ◽  
S N. Shome ◽  
S. Nandy ◽  
R. Ray
Keyword(s):  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Autonomous Navigation ◽  
Fuzzy Logic Controller ◽  
Classical Method ◽  
Obstacle Detection ◽  
Autonomous Mobile Robot ◽  
Unstructured Environment ◽  
Multiple Obstacles ◽  
Robot Platform

This paper presents a hybrid obstacle avoidance methodology for autonomous navigation of a mobile robot in an unstructured environment. Decision is taken based on the classical method depending on the environmental scenario where the space between multiple obstacles is measured and the feasibility of passing the robot through any immediate pair of obstacles examined. In other cases, the decision is taken by the Fuzzy Logic controller. The developed algorithm is simulated and experimentally validated with a mobile robot platform equipped with forward-looking sonar for obstacle detection. Odometry sensors assist in localization of the mobile robot. The developed algorithm is found adequately intelligent to navigate the robot from any start position through to the desired goal position avoiding obstacles, and without taking recourse to any pre-built map. The simulated results exhibit fair agreement with the experimental results.

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 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.

Research and Design of Range Positioning System Based on Ultrasonic Sensor for Intelligent Mobile Robot

2011 ◽  
Vol 201-203 ◽  
pp. 1886-1890 ◽  
Author(s):  
Jun Han ◽  
Rui Li Chang
Keyword(s):  
Power Consumption ◽  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Intelligent Control ◽  
Autonomous Navigation ◽  
Ultrasonic Sensor ◽  
Positioning System ◽  
Large Power ◽  
Research And Design ◽  
Important Branch

The technology of mobile robot is an important branch in robot research. In order to endow robot with capacity of intelligent control and autonomous navigation, and solve the problems such as high cost and large power consumption, a range positioning system based on ultrasonic sensor for intelligent mobile robot is designed in this paper. The system makes robot achieve some functions such as alarming, obstacle avoidance and the positioning.

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