Citation :

Aggrey Shitsukane, Calvins Otieno, James Obuhuma, Lawrence Mukhongo, Gideon Wandabwa, "Optimization of Fuzzy Logic Controllers for Autonomous Robot Navigation Using Similarity-Based Rule Simplification," International Journal of Computer Trends and Technology (IJCTT), vol. 73, no. 9, pp. 1-9, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I9P101

Abstract

Autonomous mobile robots have transformed industries by enabling operations without human intervention. Fuzzy logic controllers (FLCs) are widely used in robots for path planning due to their capability to handle uncertainties. However, large and complex fuzzy rule sets often result in computational inefficiencies, leading to increased navigational time. This study proposes a similarity-based rule reduction method to optimize fuzzy inference systems. The approach leverages a similarity threshold algorithm to identify and merge redundant rules using a similarity index. The hypothesis suggests that simplifying the rule set will either sustain or improve the robot’s task completion time. The study is validated through simulations of an FLC-based path planning nonholonomic wheeled mobile robot navigating in a static, unknown environment. The robot detects obstacles, localizes itself, and maps its surroundings in real-time to achieve effective navigation. The primary inputs are obstacle distances, and the output determines wheel velocity. MATLAB and CoppeliaSim are used to optimize and evaluate the robot’s performance, with traversal time as the primary metric. The results show that cutting down the number of fuzzy logic rules shortened the robot’s traversal time and boosted its processing speed and overall performance. By simplifying the rule base by nearly half (48.15%), the robot was able to complete its navigation tasks more quickly and efficiently. This demonstrates a practical way to tackle the long-standing issue of fuzzy rule complexity, making decision making systems more adaptive and responsive. These results are particularly significant for real-time applications where speed and dependability are crucial, like robotics, driverless cars, and predictive maintenance. 

Keywords

Fuzzy logic controller, Autonomous robot, Rule set reduction, Similarity measure.

References

[1] Anushka Biswas, and Hwang-Cheng Wang, “Autonomous Vehicles Enabled by the Integration of IoT, Edge Intelligence, 5G, and Blockchain,” Sensors, vol. 23, no. 4, pp. 1-60, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[2] Mohsen Soori et al., “Intelligent Robotic Systems in Industry 4.0: A Review,” Journal of Advanced Manufacturing Science and Technology, vol. 4, no. 3, pp. 1-29, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[3] Hooi Hung Tang, and Nur Syazreen Ahmad, “Fuzzy Logic Approach for Controlling Uncertain and Nonlinear Systems: A Comprehensive Review of Applications and Advances,” Systems Science & Control Engineering, vol. 12, no. 1, pp. 1-34, 2024.
[CrossRef] [Google Scholar] [Publisher Link] 

[4] Le Truong Giang et al., “Adaptive Spatial Complex Fuzzy Inference Systems With Complex Fuzzy Measures,” IEEE Access, vol. 11, pp. 39333-39350, 2023.
[CrossRef] [Google Scholar] [Publisher Link] 

[5] Dongrui Wu, and Jerry M. Mendel, “Similarity Measures for Closed General Type-2 Fuzzy Sets: Overview, Comparisons, and a Geometric Approach,” IEEE Transactions on Fuzzy Systems, vol. 27, no. 3, pp. 515-526, 2019.
[CrossRef] [Google Scholar] [Publisher Link]  

[6] Dimas Wibisono Prakoso, Asad Abdi, and Chintan Amrit, “Short Text Similarity Measurement Methods: A Review,” Soft Computing, vol. 25, no. 6, pp. 4699-4723, 2021.
[CrossRef] [Google Scholar] [Publisher Link]   

[7] Habiba Batti, Chiraz Ben Jabeur, and Hassene Seddik, “Autonomous Smart Robot for Path Predicting and Finding in Maze Based on Fuzzy and Neuro-Fuzzy Approaches,” Asian Journal of Control, vol. 23, no. 1, pp. 3-12, 2021.
[CrossRef] [Google Scholar] [Publisher Link]     

[8] M. Khairudin et al., “The Mobile Robot Control in Obstacle Avoidance Using Fuzzy Logic Controller,” Indonesian Journal of Science and Technology, vol. 5, no. 3, pp. 334-351, 2020.
[Google Scholar] [Publisher Link]

[9] Aggrey Shitsukane et al., “Fuzzy Logic Sensor Fusion For Obstacle Avoidance Mobile Robot,” IST-Africa Week Conference (IST-Africa), Gaborone, Botswana, pp. 1-8, 2018.
[Google Scholar] [Publisher Link]    

[10] Chian-Song Chiu, Teng-Shung Chiang, and Yu-Ting Ye, “Fuzzy Obstacle Avoidance Control of a Two-Wheeled Mobile Robot,” International Automatic Control Conference, Yilan, Taiwan, pp. 1-6, 2015.
[CrossRef] [Google Scholar] [Publisher Link]     

[11] Habiba Batti, Chiraz Ben Jabeur, and Hassene Seddik, “Mobile Robot Obstacle Avoidance in labyrinth Environment Using Fuzzy Logic Approach,” International Conference on Control, Automation and Diagnosis, Grenoble, France, pp. 1-5, 2019.
[CrossRef] [Google Scholar] [Publisher Link]   

[12] Min-You Chen, and D.A. Linkens, “Rule-Base Self-Generation and Simplification for Data-Driven Fuzzy Models,” Fuzzy Sets and Systems, vol. 142, no. 2, pp. 243-265, 2004.
[CrossRef] [Google Scholar] [Publisher Link]