Tracking the Path of a Mobile Robot Using Fuzzy Logic Algorithms
DOI:
https://doi.org/10.51173/eetj.v3i1.46Keywords:
Mobile Robot, Fuzzy Logic, Path Tracking, Intelligent Control, Autonomous NavigationAbstract
This paper presents a structured hybrid control framework for mobile robot navigation and path tracking in uncertain and dynamic environments. The proposed architecture integrates a fuzzy logic controller as the primary decisionmaking layer with a multilayer perception (MLP) neural network used for nonlinear compensation. The fuzzy module processes real-time sensory inputs from front, left, and right distance measurements and generates the initial steering command using a Mamdani inference system. To enhance tracking accuracy and robustness, the neural network is trained in a supervised manner using the mean squared error (MSE) loss function to learn adaptive correction signals. The final steering command is obtained by combining the fuzzy output with the neural compensation term, forming a hybrid control strategy that preserves interpretability while improving adaptability. The system is implemented in a simulation environment and evaluated under obstacle-rich scenarios. Quantitative performance metrics, including MSE and root mean square error (RMSE), are used to assess tracking accuracy. Experimental results demonstrate improved trajectory precision, reduced tracking error, and enhanced stability compared to standalone fuzzy and neural approaches. The proposed framework maintains clear functional separation between decision-making and compensation layers, improving modularity, transparency, and reproducibility. The results confirm that the integration of fuzzy reasoning with neural compensation provides an effective and computationally efficient solution for autonomous mobile robot navigation in nonlinear and uncertain environments.
References
J. Borenstein and Y. Koren, “The vector field histogram—Fast obstacle avoidance for mobile robots,” IEEE Transactions on Robotics and Automation, vol. 7, no. 3, pp. 278–288, 1991. doi: 10.1109/70.88137.
J. Borenstein and Y. Koren, “Real-time obstacle avoidance for fast mobile robots,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 19, no. 5, pp. 1179–1187, Sep. 1989, doi: 10.1109/21.44018.
B. Siciliano, L. Sciavicco, L. Villani, and G. Oriolo, Robotics: Modelling, Planning and Control, 2nd ed. London, U.K.: Springer, 2010, doi: 10.1007/978-1-84628-642-1.
S. M. LaValle, Planning Algorithms. Cambridge, U.K.: Cambridge Univ. Press, 2006, doi: 10.1017/CBO9780511546877.
J. J. E. Slotine and W. Li, Applied Nonlinear Control. Englewood Cliffs, NJ, USA: Prentice Hall, 1991.
M. W. Spong, S. Hutchinson, and M. Vidyasagar, Robot Modeling and Control. Hoboken, NJ, USA: Wiley, 2006.
S. Haykin, Neural Networks and Learning Machines, 3rd ed. Upper Saddle River, NJ, USA: Pearson, 2009.
C. M. Bishop, Pattern Recognition and Machine Learning. New York, NY, USA: Springer, 2006, doi: 10.1007/978-0-387-45528-0.
C. N. Kim and M. M. Trivedi, “A neuro-fuzzy controller for mobile robot navigation,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 28, no. 6, pp. 829–840, Dec. 1998, doi: 10.1109/3477.735392.
F. L. Lewis, S. Jagannathan, and A. Yesildirak, Neural Network Control of Robot Manipulators and Nonlinear Systems. London, U.K.: Taylor & Francis, 1999.
K. S. Narendra and K. Parthasarathy, “Identification and control of dynamical systems using neural networks,” IEEE Transactions on Neural Networks, vol. 1, no. 1, pp. 4–27, Mar. 1990, doi: 10.1109/72.80202.
S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics. Cambridge, MA, USA: MIT Press, 2005.
X. Yao, “Evolving artificial neural networks,” Proceedings of the IEEE, vol. 87, no. 9, pp. 1423–1447, Sep. 1999, doi:10.1109/5.784219.
P. Corke, Robotics, Vision and Control, 2nd ed. Cham, Switzerland: Springer, 2017, doi: 10.1007/978-3-319-54413-7.
B. Siciliano, L. Sciavicco, L. Villani, and G. Oriolo, Robotics: Modelling, Planning and Control, 2nd ed. London, U.K.: Springer, 2010, doi: 10.1007/978-1-84628-642-1.
I. Ulrich and J. Borenstein, “VFH+: Reliable obstacle avoidance for fast mobile robots,” in Proc. IEEE Int. Conf. Robotics and Automation (ICRA), San Francisco, CA, USA, 2000, pp. 1572–1577, doi: 10.1109/ROBOT.2000.844081.
H. Zhang and S. Wang, “Fuzzy logic-based visual navigation for mobile robots,” IEEE Transactions on Fuzzy Systems, vol. 18, no. 6, pp. 1155–1167, Dec. 2010, doi: 10.1109/TFUZZ.2010.2042282.
G. Kratzig, “Simulated pistol training: The future of law enforcement training?” International Police Training Journal, Mar. 2022.
A. H. Zahraee, J. Szewczyk, J. K. Paik, and M. Guillaume, “Robotic hand-held surgical device: Evaluation of end-effector kinematics,” in Proc. Int. Conf. Medical Image Computing and Computer-Assisted Intervention (MICCAI), Beijing, China, 2022, doi:10.1007/978-3-642-15711-0_54.
B. Siciliano, L. Sciavicco, L. Villani, and G. Oriolo, Robotics: Modelling, Planning and Control, 2nd ed. London, U.K.: Springer, 2010, doi: 10.1007/978-1-84628-642-1.
F. L. Lewis, S. Jagannathan, and A. Yesildirak, Neural Network Control of Robot Manipulators and Nonlinear Systems. London, U.K.: Taylor & Francis, 1999.
K. S. Narendra and K. Parthasarathy, “Identification and control of dynamical systems using neural networks,” IEEE Transactions on Neural Networks, vol. 1, no. 1, pp. 4–27, 1990, doi: 10.1109/72.80202.
C. N. Kim and M. M. Trivedi, “A neuro-fuzzy controller for mobile robot navigation,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 28, no. 6, pp. 829–840, 1998, doi: 10.1109/3477.735392.
S. M. LaValle, Planning Algorithms. Cambridge, U.K.: Cambridge Univ. Press, 2006, doi: 10.1017/CBO9780511546877.
P. Corke, Robotics, Vision and Control, 2nd ed. Cham, Switzerland: Springer, 2017, doi: 10.1007/978-3-319-54413-7.
L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338–353, Jun. 1965, doi: 10.1016/S0019-9958(65)90241-X.
J. Yen and R. Langari, Fuzzy Logic: Intelligence, Control, and Information. Upper Saddle River, NJ, USA: Prentice Hall, 1999.
L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965, doi: 10.1016/S0019-9958(65)90241-X.
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