The Mobile Robot Control in Obstacle Avoidance Using Fuzzy Logic Controller

M. Khairudin, R. Refalda, S. Yatmono, H. S. Pramono, A. K. Triatmaja, A Shah


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.


Experiment; fuzzy logic controller; mobile robot; obstacle

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Amelia, N., Abdullah, A. G., & Mulyadi, Y. (2019). Meta-analysis of student performance as-sessment using fuzzy logic. Indonesian Journal of Science and Technology, 4(1), 74-88.

Bakdi, A., Hentout, A., Boutami, H., Maoudj, A., Hachour, O., & Bouzouia, B. (2017). Optimal path planning and execution for mobile robots using genetic algorithm and adaptive fuzzy-logic control. Robotics and Autonomous Systems, 89, 95-109.

Basjaruddin, N. C., Kuspriyanto, K., Saefudin, D., & Putra, G. (2016). Sistem penghindar tabrakan frontal berbasis logika fuzzy. Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI), 5(3), 228-232.

Bhagat, K., Deshmukh, S., Dhonde, S., & Ghag, S. (2016). Obstacle avoidance robot. Interna-tional Journal of Science, Engineering and Technology Research (IJSETR), 5(2), 439-442.

Budiharto, W. (2015). Intelligent surveillance robot with obstacle avoidance capabilities using neural network. Computional Intelligence Neuroscience, 2015, 745823.

Chołodowicz, E., & Figurowski, D. (2017). Mobile robot path planning with obstacle avoid-ance using particle swarm optimization. Pomiary Automatyka Robotyka, 21(3) 59–68.

Ellili, W., Lachtar, A., & Samet, M. (2016). Obstacle avoidance with regard to a mobile robot’s case. International Journal of Computer Science and Information Security (IJCSIS), 14(8), 212-219.

Faisal, M., Hedjar, R., Al Sulaiman, M., & Al-Mutib, K. (2013). Fuzzy logic navigation and obstacle avoidance by a mobile robot in an unknown dynamic environment. International Journal of Advanced Robotic Systems, 10(1), 37.

Fakoor, M., Kosari, A., & Jafarzadeh, M. (2016). Humanoid robot path planning with fuzzy Markov decision processes. Journal of Applied Research and Technology, 14(5), 300-310.

Hong, C., Park, C. W., & Kim, J. H. (2016). Evolutionary dual rule-based fuzzy path planner for omnidirectional mobile robot. In 2016 IEEE International Conference on Fuzzy Sys-tems (FUZZ-IEEE) (pp. 767-774). IEEE.

Khairudin, M., Chen, G. D., Wu, M. C., & Asnawi, R. (2019). Control of a Movable Robot Head Using Vision-Based Object Tracking. International Journal of Electrical & Computer, 9(4), 2503-2512.

Mac, T. T., Copot, C., Tran, D. T., & De Keyser, R. (2017). A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization. Applied Soft Computing, 59, 68-76.

Mohamed, Z., Khairudin, M., Husain, A. R., & Subudhi, B. (2016). Linear matrix inequality-based robust proportional derivative control of a two-link flexible manipulator. Journal of Vibration and Control, 22(5), 1244-1256.

Mojaveri, H. S., & Moghimi, V. (2017). Determination of economic order quantity in a fuzzy eoq model using of gmir deffuzification. Indonesian Journal of Science and Technology, 2(1), 76-80.

Oborski, P., & Fedorczyk, T. (2015). Zmodyfikowana metoda pól potencjałowych do wyznaczania drogi robota mobilnego. Pomiary Automatyka Robotyka, 19(2), 57–64.

Szulczyński, P., Pazderski, D., & Kozłowski, K. (2011). Real-time obstacle avoidance using harmonic potential functions. Journal of Automation Mobile Robotics and Intelligent Systems, 5(3), 59-66.

Terven, J. R., Raducanu, B., Meza-de-Luna, M. E., & Salas, J. (2016). Head-gestures mirroring detection in dyadic social interactions with computer vision-based wearable devices. Neurocomputing, 175, 866-876.

Zuhrie, M. S., Basuki, I., & Anifah, L. (2017). Design of an artificial intelligence robot as a tool for teaching media based on contextual teaching and learning. Jurnal Pendidikan Teknologi dan Kejuruan, 23(4), 369-373.



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