Design of Mobile Robot Transporter Prototype Using Sensor Vision and Fuzzy Logic Method

https://doi.org/10.47194/orics.v6i4.437

Authors

Keywords:

Automated Guided Vehicle, Pixy camera, fuzzy logic control, Arduino, Motor DC

Abstract

The industrial field has experienced significant developments in the automation process, especially robots play an important role in the world of automation. One type of robot used in industry is a transporter robot. This research designs and develops a prototype mobile robot transporter that uses Pixy camera as a visual sensor and Mamdani fuzzy logic control method to control the speed of DC motor. This robot is able to move objects based on color using Arduino UNO as a microcontroller, motor driver shield L298N, two DC motors, and gripper module as actuators. The distance and speed of the robot are determined to ensure the ability to approach and move objects based on color appropriately. Testing of the robot system is done with X position value and area as parameters. Simulation of the experiment was carried out with a case study of the X position value of 73 and an area of 1012. Robot testing is done using simulation software and Arduino IDE which is then calculated manually for comparison. The results obtained in testing with simulation software are 88.70 PWM for the right DC motor and 76.10 PWM for the left DC motor. Based on the data obtained from simulation software, testing with Arduino IDE, and manual calculations, an error value of 0.158% for the right DC motor and 0.092% for the left DC motor was found. Additional tests were carried out with variations in the distance of the object being moved as well as the transfer of the object to the destination. These results show that Mamdani fuzzy logic control is effective in controlling the transporter robot, allowing accurate maneuvering and adaptive to environmental changes.

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Published

2025-12-31

How to Cite

Setiawan, A. E. ., Mardiati, R., & Firdaus, H. (2025). Design of Mobile Robot Transporter Prototype Using Sensor Vision and Fuzzy Logic Method. Operations Research: International Conference Series, 6(4), 152–162. https://doi.org/10.47194/orics.v6i4.437