Mobile robot wall-following control using a behavior-based fuzzy controller in unknown environments

Document Type: Research Paper


1 Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan City 70101, Taiwan, ROC

2 Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung City 41170, Taiwan, ROC


This paper addresses a behavior-based fuzzy controller (BFC) for mobile robot wall-following control.
The wall-following task is usually used to explore an unknown environment.
The proposed BFC consists of three sub-fuzzy controllers, including Straight-based Fuzzy Controller (SFC),
Left-based Fuzzy Controller (LFC), and Right-based Fuzzy Controller (RFC).
The proposed wall-following controller has three characteristics: the mobile robot keeps a distance from the wall,
the mobile robot has a high moving velocity, and the mobile robot has a good robustness ability of disturbance.
The proposed BFC will be used to control the real mobile robot.
The Pioneer 3-DX mobile robot has sonar sensors in front and sides, and it is used in this study.
The inputs of BFC are sonar sensors data and the outputs of BFC are robot¡¦s left/right wheel speed.
Experimental results show that the proposed BFC successfully performs the mobile robot wall-following task
in a real unknown environment.


[1] N. K. A. Al-Sahib, R. J. Ahmed, Guiding mobile robot by applying fuzzy approach on sonar sensors, AI-Khwarizmi
Engineering Journal, 6(3) (2010), 36-44.
[2] T. Balch, R. C. Arkin, Avoiding the past: A simple but effective strategy for reactive navigation, IEEE International
Conference on Robotics and Automation, 3 (1993), 588-594.
[3] Q. Y Bao, S. M. Li, W. Y. Shang, M. J. An, A fuzzy behavior-based architecture for mobile robot navigation
in unknown environments, International Conference on Arti ficial Intelligence and Computational Intelligence, 2
(2009), 257-261.
[4] F. Cupertino, T. Bari, V. Giordano, D. Naso, L. Del fine, Fuzzy control of a mobile robot, IEEE Transactions on
Robotics and Automation Magazine, 13(4) (2006), 74-81.
[5] U. Farooq, A. Khalid, M. Amar, A. Habiba, S. Sha fique, R. Noor, Design and low cost implementation of a fuzzy
logic controller for wall following behavior of a mobile robot, 2nd International Conference on Signal Processing
Systems (ICSPS2010), 2 (2010), 740-746.
[6] C. H. Hsu, C. F. Juang, Evolutionary robot wall-following control using type-2 fuzzy controller with species-DE-
activated continuous ACO, IEEE Transactions on Fuzzy Systems, 21(1) (2013), 100-112.
[7] L. Li, C. J. Lin, M. L. Huang, S. C. Kuo, Y. R. Chen, Mobile robot navigation control using recurrent fuzzy CMAC
based on improved dynamic arti ficial bee colony, Advances in Mechanical Engineering, 8(11) (2016), 1-10.
[8] K. Qian, A. Song, Autonomous navigation for mobile robot based on a sonar ring and its implementation, 8th IEEE
International Symposium on Instrumentation and Control Technology (ISICT2012), (2012), 47-50.
[9] W. Qiu, C. Zhang, Z. Ping, Generalized fuzzy time series forecasting model enhanced with particle swarm optimiza-
tion, International Journal of u- and e-Service, Science and Technology, 8(5) (2015), 129-140.
[10] S. Thongchai, S, Suksakulchai, D. M. Wilkes, N. Sarkar, Sonar behavior-based fuzzy control for a mobile robot,
IEEE International Conference on Systems, Man, and Cybernetics, 5 (2000), 3532-3537.
[11] P. Van Turennout, G. Honderd, L. J. Van Schelven, Wall-following control of a mobile robot, IEEE Robotics and
Automation, 1 (1992), 280-285.
[12] S. C. Yang, C. J. Lin, H. Y. Lin, J. G. Wang, C. Y. Yu, Image backlight compensation using recurrent functional
neural fuzzy networks based on modifi ed differential evolution, Iranian Journal of Fuzzy Systems, 13(6) (2016), 1{19.
[13] S. Yasunobu, S. Miyamoto, Automatic train operation system by predictive fuzzy control, Industrial Applications
of Fuzzy Control, M. Sugeno, Ed. Amsterdam: North-Holland, 1 (1985), 1-18.
[14] L. A. Zadeh, Fuzzy Sets, Information and Control, 8 (1965), 338-353.