Implementation of a hexapod mobile robot with a fuzzy controller

Robotica ◽  
2005 ◽  
Vol 23 (6) ◽  
pp. 681-688 ◽  
Author(s):  
Makoto Kern ◽  
Peng-Yung Woo
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Fuzzy Controller ◽  
Robot Navigation ◽  
Fuzzy Rule ◽  
Sensor Data ◽  
Rule Base ◽  
Fuzzy Rule Base ◽  
Different Types ◽  
Autonomous Robot Navigation

Fuzzy logic has features that are particular attractive in light of the problems posed by autonomous robot navigation. Fuzzy logic allows us to model different types of uncertainty and imprecision. In this paper, the implementation of a hexapod mobile robot with a fuzzy controller navigating in unknown environments is presented. The robot, MKIII, interprets input sensor data through the comparison of values in its fuzzy rule base and moves accordingly to avoid obstacles. Results of trial run experiments are presented.

scholarly journals A simple goal seeking navigation method for a mobile robot using human sense, fuzzy logic and reinforcement learning

2008 ◽  
Vol 18 (1) ◽  
pp. 23-27 ◽  
Author(s):  
Hamid Boubertakh ◽  
Mohamed Tadjine ◽  
Pierre-Yves Glorennec ◽  
Salim Labiod
Keyword(s):  
Fuzzy Logic ◽  
Reinforcement Learning ◽  
Mobile Robot ◽  
Fuzzy Controller ◽  
Learning Algorithm ◽  
Fuzzy Rule ◽  
Industrial Applications ◽  
Rule Base ◽  
Human Sense ◽  
Navigation Method

This paper proposes a new fuzzy logic-based navigation method for a mobile robot moving in an unknown environment. This method allows the robot obstacles avoidance and goal seeking without being stuck in local minima. A simple Fuzzy controller is constructed based on the human sense and a fuzzy reinforcement learning algorithm is used to fine tune the fuzzy rule base parameters. The advantages of the proposed method are its simplicity, its easy implementation for industrial applications, and the robot joins its objective despite the environment complexity. Some simulation results of the proposed method and a comparison with previous works are provided.

scholarly journals Design of Indoor Room Gas CO and SO2 Detection Based on Microcontroller Using Fuzzy Logic

2019 ◽  
Vol 125 ◽  
pp. 23013
Author(s):  
Slamet Widodo ◽  
M.Miftakhul Amin ◽  
Ahyar Supani
Keyword(s):  
Carbon Monoxide ◽  
Fuzzy Logic ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Motor Speed ◽  
Fuzzy Rule Base ◽  
A Value ◽  
Base Logic ◽  
So2 Gas ◽  
Co Gas

The incidence of poisoning due to carbon monoxide gas arising from drilling activities on the first floor of a building in the Kelapa Gading beauty clinic in Jakarta resulted in 17 people experiencing poisoning. In this study developing a device on the sensor used to detect CO and SO2 gas, in the air of a closed room using gas sensor MQ 135 and MQ 136. The results of testing the CO and SO2 gas gauges using samples of cigarette smoke and sulfur powder using MQ 135 and MQ 136 sensors with fuzzy rule base logic for motor speed to produce CO and SO2 gas, that obtained a value of 0.233 ppm SO2 gas safe conditions and gas input CO with the sensor obtained a value of 0.513 ppm, the condition is safe so that the output is 49.8 ppm, the condition of the fan blower does not rotate. Whereas when the reading value of 5.0 ppm is very concentrated and the CO gas input with the sensor is 13.8 ppm the condition is very concentrated producing an output of 228 ppm the very danger.

Fuzzy Controller Approach for DC Motor Control on Fishing Rod

2015 ◽  
Vol 2 (1) ◽  
pp. 20-28
Author(s):  
Emmanuel Ade Crisna Putra ◽  
Houtman P. Siregar
Keyword(s):  
Fuzzy Logic ◽  
Transfer Function ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Dc Motor ◽  
Step Response ◽  
Rule Base ◽  
Dc Motor Control ◽  
Automation Control

In this paper described the usable and effectiveness of automation control by using fuzzy logic controller forcontrolling the speed of DC motor that will be used on string roller of fishing rod. The transfer function of DCmotor has been obtained. For transfer function, the load of DC motor will be acted as input, and the output is thevelocity of DC motor. The fuzzy rule base then created by trial and error. The step response between fuzzy logiccontroller and without using fuzzy logic controller then obtained and compared. As a result, the fuzzy logic hassuccessfully reduced the overshoot of step response.

Fuzzy Reasoning in Mobile Robots Control

2010 ◽  
Vol 166-167 ◽  
pp. 191-196
Author(s):  
Adrian Dumitriu
Keyword(s):  
Fuzzy Logic ◽  
Data Integration ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Fuzzy Controller ◽  
Fuzzy Reasoning ◽  
Sensor Data ◽  
Rough Terrain ◽  
Acceleration Sensors ◽  
Mems Technology

The paper presents some author’s experiments carried out within the frame of a research project and destined to endow mobile robot modules with small and simple sensors to support navigation. Range sensors, proximity sensors and acceleration sensors in MEMS technology were used and Fuzzy logic has proved to be an adequate tool for sensor data integration. A Fuzzy controller has been developed and tested on a mobile robot moving on rough terrain.

scholarly journals Navigation of differential drive mobile robot on predefined, software designed path

2016 ◽  
Vol 3 (1-2.) ◽  
Author(s):  
Áron Papp ◽  
László Szilassy ◽  
József Sárosi
Keyword(s):  
Mobile Robot ◽  
Fuzzy Controller ◽  
Sensor Data ◽  
Rule Base ◽  
Servo Motor ◽  
Differential Drive ◽  
Navigation Data ◽  
Position And Orientation ◽  
Input Variables ◽  
Robot Position

This paper will be presenting the process of mobile robot movement controlling, from the task of collecting sensor data until the problem of controlling data to the servo motor controllers. In details, the first part will show the mechanism of converting CAD data to routes, and the processing of the navigation data read from the sensors and calculated from former controlling commands. The second part will explain the processing of navigation data, the applying of the actual robot position and orientation on the predefined virtual path and the production of the controller's input variables. The Fuzzy controller and the rule base will be introduced in the third part.

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