scholarly journals Reactive Obstacle-Avoidance Systems for Wheeled Mobile Robots based on Artificial Intelligence

2021 ◽  
Author(s):  
A. Medina-Santiago ◽  
Luis Alberto Morales-Rosales ◽  
Carlos Arturo Hernández-Gracidas ◽  
Ignacio Algredo-Badillo ◽  
Ana Dalia Pano-Azucena ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Obstacle Avoidance ◽  
Reactive Systems ◽  
Obstacle Detection ◽  
Depth Image ◽  
Wheeled Mobile Robots ◽  
First Case

Obstacle-avoidance robots have become an essential field of study in recent years. This paper analyzes two cases that extend reactive systems focused on obstacle detection and its avoidance. The scenarios explored get data from their environments through sensors and generate information for the models based on artificial intelligence to obtain a reactive decision. The main contribution is focused on the discussion of aspects that allow comparing both approaches, such as the heuristic approach implemented, requirements, restrictions, response time, and performance. The first case presents a mobile robot that applies fuzzy logic to achieve soft turning basing its decision on depth image information. The second case introduces a mobile robot based on multi-layer perceptron and ultrasonic sensors to decide how to move in an uncontrolled environment. The analysis of both options offers perspectives to choose between reactive obstacle-avoidance systems based on ultrasonic or Kinect sensors, models that infer optimal decisions applying fuzzy logic or artificial neural networks, with key elements and methods to design mobile robots with wheels. Therefore, we show how AI or Fuzzy Logic techniques allow us to design mobile robots that learn from their “ experience ” by making them safe and adjustable for new tasks, unlike traditional robots that use large programs to perform a specific task.

scholarly journals Reactive Obstacle–Avoidance Systems for Wheeled Mobile Robots Based on Artificial Intelligence

2021 ◽  
Vol 11 (14) ◽  
pp. 6468
Author(s):  
A. Medina-Santiago ◽  
Luis Alberto Morales-Rosales ◽  
Carlos Arturo Hernández-Gracidas ◽  
Ignacio Algredo-Badillo ◽  
Ana Dalia Pano-Azucena ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Obstacle Avoidance ◽  
Obstacle Detection ◽  
Depth Image ◽  
Inference System ◽  
First Case ◽  
Artificial Neural

Obstacle–Avoidance robots have become an essential field of study in recent years. This paper analyzes two cases that extend reactive systems focused on obstacle detection and its avoidance. The scenarios explored get data from their environments through sensors and generate information for the models based on artificial intelligence to obtain a reactive decision. The main contribution is focused on the discussion of aspects that allow for comparing both approaches, such as the heuristic approach implemented, requirements, restrictions, response time, and performance. The first case presents a mobile robot that applies a fuzzy inference system (FIS) to achieve soft turning basing its decision on depth image information. The second case introduces a mobile robot based on a multilayer perceptron (MLP) architecture, which is a class of feedforward artificial neural network (ANN), and ultrasonic sensors to decide how to move in an uncontrolled environment. The analysis of both options offers perspectives to choose between reactive Obstacle–Avoidance systems based on ultrasonic or Kinect sensors, models that infer optimal decisions applying fuzzy logic or artificial neural networks, with key elements and methods to design mobile robots with wheels. Therefore, we show how AI or Fuzzy Logic techniques allow us to design mobile robots that learn from their “experience” by making them safe and adjustable for new tasks, unlike traditional robots that use large programs to perform a specific task.

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.

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 Trajectory Tracking of Wheeled Mobile Robots Using Z-Number Based Fuzzy Logic

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 18426-18441
Author(s):  
Mohamed Abdelwahab ◽  
Victor Parque ◽  
Ahmed M. R. Fath Elbab ◽  
A. A. Abouelsoud ◽  
Shigeki Sugano
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robots ◽  
Trajectory Tracking ◽  
Wheeled Mobile Robots
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