Document Type : Research Article
Authors
1 Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
2 Department of Electrical Engineering, University of Bojnord, Bojnord, Iran
10.30473/coam.2026.77893.1411
Abstract
Non-holonomic mobile robots are widely deployed in industrial and service environments, yet designing controllers for moving-target tracking under nonholonomic constraints remains a challenging open problem. In this paper, we present a Mamdani-type fuzzy logic tracking controller specifically designed for non-holonomic mobile robots pursuing dynamic targets with arbitrary movement patterns. Although tailored to differential-drive platforms, the proposed architecture can be extended to other types of mobile robots and autonomous systems. A key feature of the controller is the explicit enforcement of a predefined safe distance between the robot and the target, preventing collision while simultaneously supporting covert or low-detection tracking applications. A significant advantage of this model-free architecture is its computational efficiency: the system operates on minimal sensor inputs, requires no dynamic model of the robot,
and can be seamlessly deployed on low-cost sensing hardware, making it well-suited for energy-constrained platforms. Lyapunov-based stability analysis is provided for the closed-loop system, and the methodology is validated through simulation on a differential-drive robot model across multiple complex scenarios in a virtual environment, including cases with high measurement noise. The comprehensive simulation results confirm that the controller achieves robust stability and high tracking precision, demonstrating its practical acceptability for real-time target tracking applications.
Highlights
- A Mamdani-type fuzzy logic controller is proposed for model-free, real-time tracking of arbitrarily moving targets using non-holonomic mobile robots operating in polar coordinates
- The controller extends target type from non-holonomic robots (as in most prior work) to arbitrary real-world objects (pedestrians, vehicles, rolling objects), increasing practical applicability.
- An explicit minimum safety distance constraint is incorporated into the fuzzy rule design to prevent collision, providing collision avoidance in addition to tracking.
- The model-free architecture requires only distance and bearing angle from sensors, enabling low-cost and energy-efficient implementation on resource-constrained platforms.
- Robustness to high measurement noise (up to ~50% signal contamination) is validated via MATLAB/Simulink simulations across three distinct target-path scenarios.
Keywords
Main Subjects
[1] Ali, N. (2020). “Fuzzy logic based real time go to goal controller for mobile robot”. International Journal of Computer Applications, 176(11), 32–36. https://doi.org/10. 5120/ijca2020920078
[2] Asif, M., Khan, M.J., Safwan, M., Rehan, M. (2015). “Feedforward and feedback kinematic controllers for wheeled mobile robot trajectory tracking”. Journal of Automation and Control Engineering Vol, 3(3). https://doi.org/10.12720/joace.3.3. 178-182
[3] Benbouabdallah, K., Zhu, Q.D. (2013). “Particle swarm optimization of a fuzzy logic controller for a mobile robot tracking a moving target”. Applied Mechanics and Materials, 373, 1237–1241. https://doi.org/10.4028/www.scientific.net/AMM.373-375. 1237
[4] Boumehraz, M., Habba, Z., Hassani, R. (2018). “Vision based tracking and interception of moving target by mobile robot using fuzzy control”. Journal of Applied Engineering Science and Technology, 4(2), 7–7. https://doi.org/10.69717/jaest.v4.i2.79
[5] Campion, G., Bastin, G., Dandrea-Novel, B. (1996). “Structural properties and classification of kinematic and dynamic models of wheeled mobile robots”. IEEE transactions on robotics and automation, 12(1), 47–62. https://doi.org/10.1109/70.481750
[6] Chen, Z., Liu, Y., He, W., Qiao, H., Ji, H. (2021). “Adaptive-neural-network-based trajectory tracking control for a nonholonomic wheeled mobile robot with velocity constraints”. IEEE Transactions on Industrial Electronics, 68(6), 5057–5067. https://doi.org/10. 1109/TIE.2020.2989711
[7] Cui, M., Liu, H., Liu, W., Qin, Y. (2018). “An adaptive unscented Kalman filter-based controller for simultaneous obstacle avoidance and tracking of wheeled mobile robots with unknown slipping parameters”. Journal of Intelligent & Robotic Systems, 92(3), 489–504. https://doi.org/10.1007/s10846-017-0761-9
[8] Dong, L., He, Z., Song, C., Sun, C. (2023). “A review of mobile robot motion planning methods: from classical motion planning workflows to reinforcement learning based architectures”. Journal of Systems Engineering and Electronics, 34(2), 439–459. https://doi.org/10.23919/JSEE.2023.000051
[9] Dong, X., Shen, J., Wang, W., Shao, L., Ling, H., Porikli, F. (2019). “Dynamical hyperparameter optimization via deep reinforcement learning in tracking”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(5), 1515–1529. https://doi.org/ 10.1109/TPAMI.2019.2956703
[10] Drake, D., Koziol, S., Chabot, E. (2018). “Mobile robot path planning with a moving goal”. IEEE Access, 6, 12800–12814. https://doi.org/10.1109/ACCESS.2018.2797070
[11] Fu, Y.-K., Liu, Y., Deng, C. (2025). “Deep reinforcement learning-based cooperative control for multimobile robots with obstacle avoidance”. IEEE Transactions on Industrial Electronics, 72(12), 14563–14571. https://doi.org/10.1109/TIE.2025.3581265
[12] Grau, A., Indri, M., Lo Bello, L., Sauter, T. (2021). “Robots in industry: The past, present, and future of a growing collaboration with humans”. IEEE Industrial Electronics Magazine, 15(1), 50–61. https://doi.org/10.1109/MIE.2020.3008136
[13] Guan, R., Hu, G. (2023). “Formation tracking of mobile robots under obstacles using only an active RGB-D camera”. IEEE Transactions on Industrial Electronics, 71(4), 4049– 4058. https://doi.org/10.1109/TIE.2023.3279566
[14] Hacene, N., Mendil, B. (2021). “Behavior-based autonomous navigation and formation control of mobile robots in unknown cluttered dynamic environments with dynamic target tracking”. International Journal of Automation and Computing, 18(5), 766–786. https://doi.org/10.1007/s11633-020-1264-x
[15] Hewawasam, H.S., Ibrahim, M.Y., Appuhamillage, G.K. (2022). “Past, present and future of path-planning algorithms for mobile robot navigation in dynamic environments”. IEEE Open Journal of the Industrial Electronics Society, 3, 353–365. https://doi.org/10. 1109/OJIES.2022.3179617
[16] Jayasree, K.R., Jayasree, P.R., Vivek, A. (2017). “Dynamic target tracking using a four wheeled mobile robot with optimal path planning technique”. In the 2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT) (pp. 1–6). IEEE. https://doi.org/10.1109/ICCPCT.2017.8074365
[17] Jianhua, W., Robert, W. (2010). “Dynamic obstacle avoidance for an omnidirectional mobile robot”. Journal of Robotics, 22, 1–14. https://doi.org/10.1155/2010/901365
[18] Kaliński, K.J., Mazur, M. (2016). “Optimal control at energy performance index of the mobile robots following dynamically created trajectories”. Mechatronics, 37, 79–88. https://doi.org/10.1016/j.mechatronics.2016.01.006
[19] Li, X., Xu, Z., Li, S., Su, Z., Zhou, X. (2021). “Simultaneous obstacle avoidance and target tracking of multiple wheeled mobile robots with certified safety”. IEEE Transactions on Cybernetics, 52(11), 11859–11873. https://doi.org/10.1109/TCYB.2021.3070385
[20] Lin, Y.-W., Wai, R.-J. (2013). “Design of adaptive moving-target tracking control for vision-based mobile robot”. In 2013 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) (pp. 194–199). IEEE. https://doi.org/10.1109/ CICA.2013.6611684
[21] Lv, Y.-W., Yang, G.-H., Wasly, S. (2023). “Cooperative localization and target tracking in mobile sensor networks with uncertainties in measurement models”. IEEE Transactions on Vehicular Technology, 72(12), 15521–15534. https://doi.org/10.1109/ TVT.2023.3290959
[22] Maatoug, K., Njah, M., Jallouli, M. (2015). “Free navigation and obstacle avoidance based on fuzzy controller”. In Proc. of the 2nd World Congress on Computer Application and International Systems. https://doi.org/06.WCCAIS.2014.1.9
[23] Matsubara, Y., Tamura, H., Minami, T., Nakauchi, S. (2026). “Subjective emotions and physiological responses during collision avoidance with a virtual autonomous mobile robot”. International Journal of Social Robotics, 18(1), 5. https://doi.org/10.1007/ s12369-025-01341-3
[24] Nikolaev, M.S., Matveev, A.S. (2019). “Provable reactive navigation of mobile robots to a moving target in unpredictable dynamic scenes”. In 2019 IEEE 58th Conference on Decision and Control (CDC) (pp. 220–225). IEEE. https://doi.org/10.1109/CDC40024. 2019.9029995
[25] Park, S., Lee, S.-M. (2023). “Formation reconfiguration control with collision avoidance of nonholonomic mobile robots”. IEEE Robotics and Automation Letters, 8(12), 7905– 7912. https://doi.org/10.1109/LRA.2023.3324593
[26] Petrović, E., Simonović, M., Nikolić, V. (2017). “Fuzzy control of differential drive mobile robot for moving target tracking”. Facta Universitatis, Series: Automatic Control and Robotics, 16(2), 83–93. https://doi.org/10.22190/FUACR1702083P
[27] Sachin, V., Hashir, A.K., Mohammed, S.O., Vishnu, R., Syed Muhammed, F., Imthias, A.T.P. (2022). “Motion planning & obstacle avoidance of mobile robot in a stochastic environment using reinforcement learning”. In Proceedings of the International Conference on Aerospace & Mechanical Engineering (ICAME 21).
[28] Tiriolo, C., Lucia, W. (2023). “A set-theoretic control approach to the trajectory tracking problem for input–output linearized wheeled mobile robots”. IEEE Control Systems Letters, 7, 2347–2352. https://doi.org/10.1109/LCSYS.2023.3286774
[29] Tolossa, T.D., Gunasekaran, M., Halder, K., Verma, H.K., Parswal, S.S., Jorwal, N., Joseph, F.O.M., Hote, Y.V. (2024). “Trajectory tracking control of a mobile robot using fuzzy logic controller with optimal parameters”. Robotica, 42(8), 2801–2824. https: //doi.org/10.1017/S0263574724001140
[30] Vanhie-Van Gerwen, J., Geebelen, K., Wan, J., Joseph, W., Hoebeke, J., De Poorter, E. (2021). “Indoor drone positioning: Accuracy and cost trade-off for sensor fusion”. IEEE Transactions on Vehicular Technology, 71(1), 961–974. https://doi.org/10.1109/ TVT.2021.3129917
[31] Wang, L.-X. (1996). A course in fuzzy systems and control. Prentice-Hall, Inc. ISBN: 0135408822
[32] Wang, Y., Shan, M., Yue, Y., Wang, D. (2020). “Autonomous target docking of nonholonomic mobile robots using relative pose measurements”. IEEE Transactions on Industrial Electronics, 68(8), 7233–7243. https://doi.org/10.1109/TIE.2020.3001805
[33] Xu, S., Peng, H. (2019). “Design, analysis, and experiments of preview path tracking control for autonomous vehicles”. IEEE Transactions on Intelligent Transportation Systems, 21(1), 48–58. https://doi.org/10.1109/TITS.2019.2892926
[34] Yun, S., Choi, J., Yoo, Y., Yun, K., Choi, J.Y. (2018). “Action-driven visual object tracking with deep reinforcement learning”. IEEE Transactions on Neural Networks and Learning Systems, 29(6), 2239–2252. https://doi.org/10.1109/TNNLS.2018.2801826
[35] Zhang, H.-M., Li, M.-L. (2017). “Rapid path planning algorithm for mobile robot in dynamic environment”. Advances in Mechanical Engineering, 9(12), 1687814017747400. https://doi.org/10.1177/1687814017747400
[36] Zhang, K., Gao, R., Zhang, J. (2020). “Research on trajectory tracking and obstacle avoidance of nonholonomic mobile robots in a dynamic environment”. Robotics, 9(3), 74. https://doi.org/10.3390/robotics9030074
[37] Zhang, B., Okutsu, M., Ochiai, R., Tayama, M., Lim, H.-O. (2023). “Research on design and motion control of a considerate guide mobile robot for visually impaired people”. IEEE Access, 11, 62820–62828. https://doi.org/10.1109/ACCESS.2023.3288152
[38] Zhou, H., Feng, P., Chou, W. (2023). “A hybrid obstacle avoidance method for mobile robot navigation in unstructured environment”. Industrial robot: The international journal of robotics research and application, 50(1), 94–106. https://doi.org/10.1108/ IR-04-2022-0102
)