Main Article Content

Mahabbat Khudaverdiyeva


This paper presents the modeling of a robot's navigation using ultrasonic sensors under uncertainty. The robot tries to avoid obstacles by using the fuzzy logic controller to process the data coming from three ultrasonic sensors. To assess the performance of fuzzy logic optimized robot navigation controller with ultrasonic sensors, which measure the distance by calculating the time spent on the object and its return, the obstacles are placed in front of, left, and right of the robot.  Mamdani fuzzy reasoning system is used for the designed controller for its intuitive properties and fewer setting parameters which reduces the amount of time spent on the programming of the controller. 25 rules are considered to cover a robot’s possible interactions with obstacles. For an easy understanding of navigation architecture and rapid algorithm implementation, in this paper, a MATLAB simulation framework is developed. MATLAB/Simulink is one of the best simulation tools required to design the architecture and verify algorithms with real-time constraints. Resultant models of the fuzzy optimized controller demonstrate the superior performance of the fuzzy logic controller with high adaptability to the environment while maintaining a sufficient level of accuracy. The designed fuzzy controller can be used in microprocessor/microcontroller-based robots owing to easiness in implementation and coding.

Article Details

How to Cite
Khudaverdiyeva, M. (2022). MODELING OF MOBILE ROBOT WITH OBSTACLE AVOIDANCE USING FUZZY CONTROLLER . Advanced Information Systems, 6(2), 21–25.
Information systems modeling
Author Biography

Mahabbat Khudaverdiyeva, Azerbaijan State Oil and Industry University, Baku

Head of the teaching laboratory, candidate for PhD, Instrumentation Engineering Department


Yan, H., Zhu, Q., Zhang, Y., Li, Z. and Du, X. (2022), "An Obstacle Avoidance Algorithm for Unmanned Surface Vehicle Based on A Star and Velocity-Obstacle Algorithms", 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC), pp. 77-82, DOI:

Ribeiro, T., Gonçalves, F., Garcia, I., Lopes, G. and Ribeiro, A. F. (2019), "Q-Learning for Autonomous Mobile Robot Obstacle Avoidance", 2019 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 1-7, DOI:

Liu, Y., Chen, D. and Zhang, S. (2018), "Obstacle avoidance method based on the movement trend of dynamic obstacles", 2018 3rd International Conference on Control and Robotics Engineering (ICCRE), pp. 45-50, DOI:

Nan, J. (2021), "Research on Robot Obstacle Avoidance System Based on Computer Path Planning", 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA), pp. 909-913, DOI:

Cui, W. (2019), "Multi-sensor Information Fusion Obstacle Avoidance based on Fuzzy Control", 2019 IEEE 2nd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE), pp. 198-202, DOI:

Arif, M. U. and Marzoughi, A. (2020), "Arithmetic mean-based decision-making algorithm for obstacle avoidance and multiple intruder detection", 2020 Australian and New Zealand Control Conference (ANZCC), pp. 103-107, DOI:

Babinski, D., Berisha, J., Zaev, E. and Bajrami, X. (2020), "Application of Fuzzy Logic and PID Controller for Mobile Robot Navigation", 2020 9th Mediterranean Conference on Embedded Computing (MECO), pp. 1-4, DOI:

Allagui, N. Y., Abid, D. B. and Derbel, N. (2019), "Autonomous navigation of mobile robot with combined fractional-order PI and fuzzy logic controllers", 2019 16th International Multi-Conference on Systems, Signals & Devices (SSD), pp. 78-83, DOI:

Waga, A., Laminin, C., Benhlima, S. and Bekri, A. (2021), "Fuzzy logic obstacle avoidance by an NAO robot in an unknown environment", 2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS), pp. 1-7, DOI:

Zhu, Z., Xie, J. and Wang, Z. (2019), "Global Dynamic Path Planning Based on Fusion of A Algorithm and Dynamic Window Approach", 2019 Chinese Automation Congress (CAC), pp. 5572-5576, DOI:

Park, S. H. and Kim, G. W. (2014), “Expanded Guide Circle-based obstacle avoidance for the remotely operated mobile robot”, Journal of Electrical Engineering and Technology, vol. 9, no. 3, pp. 1034-1042.

Kim, D. G. and Kim, G. W. (20160, “Multi-Expanded Guide Circle-based Obstacle Avoidance for the Remotely Operated Mobile Robot", 2016 Conference on Information and Control Systems, pp. 84-85.

Lee, D. Y., Lu, Y. F., Kang, T. K., Choi, I. H. and Lim, M. T. (2012), "3D vision-based local obstacle avoidance method for a humanoid robot", 2012 12th International Conference on Control, Automation and Systems, pp. 473-475.

Luo, R. C. and Kuo, C. (2016), “Intelligent Seven-DoF Robot With Dynamic Obstacle Avoidance and 3-D Object Recognition for Industrial Cyber-Physical Systems in Manufacturing Automation”, Proceedings of the IEEE, vol. 104, no. 5, pp. 1102-1113, May 2016, DOI:

Song, K., Chang, Y. and Chen, J. (2019), "3D Vision for Object Grasp and Obstacle Avoidance of a Collaborative Robot", 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp. 254-258, DOI:

Wehrmann, D., Hildebrandt, A. -C., Wittmann, R., Sygulla, F., Rixen, D. and Buschmann, T. (2016), "Fast object approximation for real-time 3D obstacle avoidance with biped robots", 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), pp. 38-45, DOI:

Li, Y. and Liu, Y. (2020), "Vision-based Obstacle Avoidance Algorithm for Mobile Robot", 2020 Chinese Automation Congress (CAC), pp. 1273-1278, DOI:

Nagarajan, V. R. and Singh, P. (2021), "Obstacle Detection and Avoidance For Mobile Robots Using Monocular Vision", 2021 8th International Conference on Smart Computing and Communications (ICSCC), pp. 275-279, DOI:

Ghorpade, D., Thakare, A. D. and Doiphode, S. (2017), "Obstacle Detection and Avoidance Algorithm for Autonomous Mobile Robot using 2D LiDAR," 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA), pp. 1-6, DOI:

Padgett, S. T. and Browne, A. F. (2017), "Vector-based robot obstacle avoidance using LIDAR and mecanum drive", SoutheastCon 2017, pp. 1-5, DOI: