Home /Research /Fuzzy Logic Control for Semi-Autonomous Navigation Robot Using Integrated Remote Control
OTHER

Fuzzy Logic Control for Semi-Autonomous Navigation Robot Using Integrated Remote Control

Farhan Wildani, Rina Mardiati, Edi Hendri Mulyana, Aan Eko Setiawan, Rin Rin Nurmalasari, Nike Sartika

Year
2022
Citations
6

Abstract

Along with the times, technology in the field of mobile robots continues to progress very rapidly. In the development of autonomous robots, navigation is one part that has an important role. Therefore, mobile robots must be able to adapt to their environment. So we need a control method that can help the robot in the process of adjusting the dynamics of the surrounding environment. In this study, the semi-automatic navigation robot adopts artificial intelligence Fuzzy logic as an output processor that will be generated by the robot. Fuzzy logic on this robot is used to control the speed of the motor based on the distance of the obstacle that is read by the sensor and the input provided by the remote control. In this research, one ultrasonic sensor HC-SR04 is used which is mounted on the front of the robot, and remote control to give commands to the robot and Arduino MEGA 2560 as the microcontroller. To make the robot’s movement more stable when avoiding obstacles, a fuzzy logic algorithm is applied to control the right and left motor PWM. Fuzzy robot system testing is carried out with a robot scenario detecting obstacles at a distance of 4cm and the remote providing an input value of 1870Hz. The results shown on the Arduino IDE application serial monitor are 62.5 PWM for the right motor and 103.53 PWM for the left motor, while the simulation results in the Matlab application show that the right motor PWM is 62.5 PWM and the left motor is 104 PWM. By comparing the output of the semi-automatic navigation robot based on Fuzzy with the output of the simulation results, it is found that fuzzy logic has been successfully implemented on the robot with a success rate of 100% for the right motor and 99.995% for the left motor.

Keywords

RobotMobile robotFuzzy logicArduinoComputer scienceRobot controlControl engineeringFuzzy control systemRemote controlPulse-width modulation

Related papers

Browse all OTHER papers