Obstacle Avoidance using Fuzzy Logic Controller on Wheeled Soccer Robot

Noorman Rinanto, Irfan Marzuqi, Agus Khumaidi, Sryang T Sarena


The purpose of this study is to apply Fuzzy Logic Controller on a wheeled soccer robot to avoid the collision with other robots in the field. The robot equipped by an omnidirectional camera as a vision sensor, a mini-PC for the image processing device, a microcontroller to handle I/O system, and three wheel's omnidirectional mover system. Omni-camera produces four input-values, namely: X coordinate ball position, Y coordinate ball position, distance and angle from obstacle to the point of interest in the camera frame. These inputs processed by a mini-PC and then forward to a microcontroller to calculate the output using Fuzzy Logic Controller. The output variables are the movement rate of the robot in the X, and Y coordinate.  These outputs will be used by the kinematics controller to manage the speed of three Omni-wheels driven by 24 volts DC motors. The experiment shows a good result with the percentage of the success of the robot catching the ball is around 70% and 80% in avoiding the obstacle. In time performance, the soccer robot with Fuzzy Logic Controller is superior by 4.67 seconds compared to the robot without this method.


Fuzzy Logic; Soccer Robot; Omnidirectional Camera; Microcontroller; Obstacle Avoidance

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A. Jiang, X. Yao, and J. Zhou, “Research on path planning of real-time obstacle avoidance of mechanical arm based on genetic algorithm,” Journal of Engineereing., vol. 2018, no. 16, pp. 1579–1586, 2018, doi: 10.1049/joe.2018.8266.

M. Furci, D. Bicego, and A. Franchi, “Design and Input Allocation for Robots with Saturated Inputs via Genetic Algorithms,” IFAC-PapersOnLine, vol. 51, no. 22, pp. 459–464, 2018, doi: 10.1016/j.ifacol.2018.11.591.

P. P. Rebouças Filho, S. P. Suane, V. N. Praxedes, J. Hemanth, and V. H. C. de Albuquerque, “Control of singularity trajectory tracking for robotic manipulator by genetic algorithms,”Journal of Computational Science., vol. 30, pp. 55–64, 2019, doi: 10.1016/j.jocs.2018.11.006.

R. Y. Putra et al., “Neural network implementation for invers kinematic model of arm drawing robot,” in 2016 International Symposium on Electronics and Smart Devices, ISESD 2016, 2017, pp. 153–157, doi: 10.1016/j.jocs.2018.11.006.

J. Yu, J. Ji, Z. Miao, and J. Zhou, “Neural network-based region reaching formation control for multi-robot systems in obstacle environment,” Neurocomputing, vol. 333, pp. 11–21, 2019, doi: 10.1016/j.neucom.2018.12.051.

ByungSoo Ko, Ho-Jin Choi, C. Hong, J. Kim, Oh Chul Kwon, and C. D. Yoo, “Neural network-based autonomous navigation for a homecare mobile robot,” in 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 403–406, 2017, doi: 10.1109/BIGCOMP.2017.7881744.

L. Kong, W. He, C. Yang, Z. Li, and C. Sun, “Adaptive Fuzzy Control for Coordinated Multiple Robots With Constraint Using Impedance Learning,” IEEE Trans. Cybern., vol. 49, no. 8, pp. 3052–3063, Aug. 2019, doi: 10.1109/TCYB.2018.2838573.

M. A. Al Mamun, M. T. Nasir, and A. Khayyat, “Embedded System for Motion Control of an Omnidirectional Mobile Robot,” IEEE Access, vol. 6, pp. 6722–6739, 2018, doi: 10.1109/ACCESS.2018.2794441.

J. C. Mohanta and A. Keshari, “A knowledge based fuzzy-probabilistic roadmap method for mobile robot navigation,” Applied Soft Computing., vol. 79, pp. 391–409, 2019, doi: 10.1016/j.asoc.2019.03.055.

A. Shitsukane, W. Cheruiyot, C. Otieno, and M. Mvurya, “Fuzzy Logic Sensor Fusion for Obstacle Avoidance Mobile Robot,” in 2018 IST-Africa Week Conference (IST-Africa), p.p 1-8, 2018, doi: Google Scholar.

A. O. Pizarro-Lerma, R. García-Hernández, and V. A. Santibáñez, “Fine-Tuning of a Fuzzy Computed-Torque Control for a 2-DOF Robot via Genetic Algorithms,” IFAC-PapersOnLine, vol. 51, no. 13, pp. 326–331, 2018, doi: 10.1016/j.ifacol.2018.07.299.

H. Wang, J. Duan, M. Wang, J. Zhao, and Z. Dong, “Research on Robot Path Planning Based on Fuzzy Neural Network Algorithm,” in 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pp. 1800–1803, 2018, doi: 10.1109/IAEAC.2018.8577599.

D. R. Bruno, N. Marranghello, F. S. Osorio, and A. S. Pereira, “Neurogenetic algorithm applied to Route Planning for Autonomous Mobile Robots,” in 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8, 2018, doi: 10.1109/IJCNN.2018.8489137.

Rahib H. Abiyev, Irfan S. Günsel, Nurullah Akkaya, Ersin Aytac, Ahmet Çağman, Sanan Abizada, "Fuzzy control of omnidirectional robot," Procedia Computer Science,"Volume 120, pp. 608-616, 2017, doi: 10.1016/j.procs.2017.11.286.

A. Ansari and A. A. Bakar, "A Comparative Study of Three Artificial Intelligence Techniques: Genetic Algorithm, Neural Network, and Fuzzy Logic, on Scheduling Problem," 2014 4th International Conference on Artificial Intelligence with Applications in Engineering and Technology, Kota Kinabalu, pp. 31-36, 2014, doi: 10.1109/ICAIET.2014.15

DOI: http://dx.doi.org/10.26555/jiteki.v5i1.13298

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Jurnal Ilmiah Teknik Elektro Komputer dan Informatika
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