1. SISN
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  3. Volume 16, 2024
  4. Base Components of the Neuro-fuzzy Control System for a Group of Mobile Robotic Platforms

Base Components of the Neuro-fuzzy Control System for a Group of Mobile Robotic Platforms

SISN.
2024;
Volume 16
: pp. 348 - 368
Authors:
  1. Ivan Tsmots
  2. Yurii Opotyak
  3. Oleksandr Oliinyk
  4. Volodymyr Antoniv
  5. Andrii Lytvyn
1
Lviv Polytechnic National University, Lviv, Ukraine
2
Lviv Polytechnic National University, Department of Automated Control Systemst
3
Lviv Polytechnic National University, Department of Automated Control Systemst
4
Lviv Polytechnic National University, Lviv, Ukraine
5
Lviv Polytechnic National University, Department of Publishing Information Technologiest

Coordinating the movement of mobile robotic platforms (MRPs) in dynamic environments is a significant challenge in both civil and military applications, where large-scale transport, exploration, and task distribution are required. This research presents a neuro-fuzzy control system that integrates fuzzy logic with real-time navigation to optimize group movement. The system’s key components include data acquisition from navigation sensors such as gyroscopes, digital compasses, and lidars, along with wireless communication modules to facilitate seamless interaction and coordination among MRPs. A fuzzy logic controller, enhanced by neuro-like defuzzification, improves decision-making precision and platform synchronization. Additionally, the system incorporates advanced route planning algorithms to effectively manage group navigation, even in unpredictable and rapidly changing environments. The practical implementation is based on embedded platforms, including Raspberry Pi and microcontrollers such as STM8S003F3 and ESP32C3, which process data from sensors like the MPU-6050 gyroscope, QMC5883L compass, and YDLidar X4 lidar. This architecture was experimentally validated across real-world scenarios, demonstrating significant improvements in movement coordination, reduced response time, and enhanced operational efficiency. The system supports parallel processing and real-time optimization, making it suitable for tasks that require rapid adaptation to changing conditions. Furthermore, its scalability and flexibility make it an effective solution for real-world applications in environments that demand precise group control. The results underscore the practical value of this approach, reducing both development time and costs while improving the overall performance of MRP systems in complex operational settings. The developed neuro-fuzzy system provides a robust and scalable platform for efficient group management, making it well-suited for a wide range of dynamic, real-time applications.

group control
mobile robotic platforms
hybrid control
neuro-fuzzy control
  1. Cortes, J., & Egerstedt, M. (2017). Coordinated Control of Multi-Robot Systems: A Survey. SICE Journal of Control, Measurement, and System Integration, 10(6), 495–503. https://doi.org/10.9746/jcmsi.10.495
  2. Ballotta, L., & Talak, R. (2024). Safe Distributed Control of Multi-Robot Systems with Communication Delays. arXiv. https://doi.org/10.48550/arXiv.2402.09382
  3. Dong, Y., Li, Z., Zhao, X., Ding, Z., & Huang, X. (2023). Decentralised and cooperative control of multi- robot systems through distributed optimisation. arXiv. https://doi.org/10.48550/arXiv.2302.01728
  4. Ibrahim, A., Alexander, R., Shahid, M., Sanghar, U., Dsouza, R., & Souza, D. (2016). Control Systems in Robotics: A Review. International Journal of Engineering Inventions, 5, 2278-7461
  5. Osadchy, S. I., Zozulya, V. A., Ladanyuk, A. P., & et al. (2019). Optimal Robust Control of a Robots Group. Automatic Control and Computer Sciences, 53, 298-309. https://doi.org/10.3103/S0146411619040084
  6. Petrenko, D. V., & Protasov, A. G. (2024). Overview of Modern Technologies for Increasing the Autonomy of Mobile Wheeled Robots. Scientific Notes of Taurida National V.I. Vernadsky University. Series:  Technical Sciences, 2, 122–128. https://doi.org/10.32782/2663-5941/2024.2/17
  7. Mohaghegh, M., et al. (2023). Optimal predictive neuro-navigator design for mobile robot navigation with moving obstacles. Frontiers in Robotics and AI, 10. https://doi.org/10.3389/frobt.2023.1226028
  8. Jiang, C., Huang, X., & Guo, Y. (2023). End-to-end decentralized formation control using a graph neural network-based learning method. Frontiers in Robotics and AI, 10, 1285412. https://doi.org/10.3389/ frobt.2023.1285412
  9. Blumenkamp, J., Morad, S. D., Gielis, J., Li, Q., & Prorok, A. (2021). A Framework for Real-World Multi- Robot Systems Running Decentralized GNN-Based Policies. 2022 International Conference on Robotics and Automation (ICRA), 8772–8778. https://doi.org/10.48550/arXiv.2111.01777
  10. Omrane, H., Masmoudi, M., & Masmoudi, M. (2016). Fuzzy Logic Based Control for Autonomous Mobile Robot Navigation. Computational Intelligence and Neuroscience, 2016, 1–10. https://doi.org/10.1155/2016/9548482
  11. Wang, L., Chen, M., Li, G., & Fan, Y. (2016). Data-Based Control for Humanoid Robots Using Support Vector Regression, Fuzzy Logic, and Cubature Kalman Filter. Mathematical Problems in Engineering, 2016, Article ID 1984634. https://doi.org/10.1155/2016/1984634
  12. Lillian, M., et al. (2021). Navigational Behavior of Humans and Deep Reinforcement Learning Agents. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.725932
  13. Carli, R., Cavone, G., Epicoco, N., Di Ferdinando, M., Scarabaggio, P., & Dotoli, M. (2020). Consensus- Based Algorithms for Controlling Swarms of Unmanned Aerial Vehicles. In: Grieco, L.A., Boggia, G., Piro, G., Jararweh, Y., & Campolo, C. (Eds). Ad-Hoc, Mobile, and Wireless Networks. ADHOC-NOW 2020. Lecture Notes in Computer Science, vol 12338. Springer, Cham. https://doi.org/10.1007/978-3-030-61746-2_7
  14. Kotani, K., & Namerikawa, T. (2019). Cooperative Transport of Quad-rotor by Consensus Algorithm.2019 12th Asian Control Conference (ASCC), 1364-1369.
  15. Sell, R., Väljaots, E., Pataraia, T., & Malayjerdi, E. (2019). Modular Smart Control System Architecture for the Mobile Robot Platform. Proceedings of the Estonian Academy of Sciences, 68, 395. https://doi.org/10.3176/proc.2019.4.08
  16. Tzafestas, S. G. (2018). Mobile Robot Control and Navigation: A Global Overview. Journal of Intelligent & Robotic Systems, 91, 35–58. https://doi.org/10.1007/s10846-018-0805-9
  17. Razzaq, Z., Brahimi, N., Rehman, H., & Khan, Z. (2024). Intelligent Control System for Brain-Controlled Mobile Robot Using Self-Learning Neuro-Fuzzy Approach. Sensors, 24. https://doi.org/10.3390/s24185875
  18. Samadi, M., & Jond, H. (2021). An Intelligent Approach for Autonomous Mobile Robots Path Planning Based on Adaptive Neuro-Fuzzy Inference System. Ain Shams Engineering Journal. https://doi.org/10.1016/j.asej.2021.05.005
  19. Stingu, P. E., & Lewis, F. L. (2009). Neuro-fuzzy Control of Autonomous Robotics. In: Meyers, R. (Eds). Encyclopedia of Complexity and Systems Science. Springer, New York, NY. https://doi.org/10.1007/978-0-387- 30440-3_357
  20. Hasibuan, A., Nasution, T., & Azis, P. (2023). MPU-6050 Wheeled Robot Controlled Hand Gesture Using L298N Driver Based on Arduino. Journal of Physics: Conference Series, 2421. https://doi.org/10.1088/1742- 6596/2421/1/012022
  21. Ammar, A., Yusril, M., & Broto, P. (2023). Prototype of Automatic Tractor Control Navigation System Using ESP 32 Microcontroller. Al-Fiziya: Journal of Materials Science, Geophysics, Instrumentation and Theoretical Physics, 6, 20-29. https://doi.org/10.15408/fiziya.v6iI.31593
  22. Park, G., & Chung, W. (2024). Uncertainty aware LiDAR based Localization for Outdoor Mobile Robots. Journal of Field Robotics. https://doi.org/10.1002/rob.22392
  23. Yousuf, S., & Kadri, M. B. (2021). Information Fusion of GPS, INS and Odometer Sensors for Improving Localization Accuracy of Mobile Robots in Indoor and Outdoor Applications. Robotica, 39(2), 250-276. https://doi.org/10.1017/S0263574720000351
  24. Yang, L., Li, P., Qian, S., Quan, H., Miao, J., Liu, M., Hu, Y., & Memetimin, E. (2023). Path Planning Technique for Mobile Robots: A Review. Machines, 11, 980. https://doi.org/10.3390/machines11100980
  25. Agung, F., Herizon, H., Madona, E., Rohfadli, M., & Ja’far, J. (2023). Implementation of LiDAR Sensor for Mobile Robot Delivery Based on Robot Operating System. JECCOM: International Journal of Electronics Engineering and Applied Science, 1(2), 67–79. https://doi.org/10.30630/jeccom.1.2.67-79.2023
  26. Chen, C.-H., Wang, C.-C., Wang, Y. T., & Wang, P. T. (2017). Fuzzy Logic Controller Design for Intelligent Robots. Mathematical Problems in Engineering, 2017, Article ID 8984713, 12 pages. https://doi.org/10.1155/2017/8984713
  27. Hentout, A., Maoudj, A. & Aouache, M. A (2022) review of the literature on fuzzy-logic approaches for collision-free path planning of manipulator robots. Artif Intell Rev 56, 3369–3444. https://doi.org/10.1007/s10462- 022-10257-7
  28. Li, Y., Song, G., Yip, T. L., & Yeo, G. T. (2024). Fuzzy Logic-Based Decision-Making Method for Ultra- Large Ship Berthing Using Pilotage Data. Journal of Marine Science and Engineering, 12(5), 717. https://doi.org/10.3390/jmse12050717
  29. Tkachenko, R., & Izonin, I. (2018). Model and Principles for the Implementation of Neural-Like Structures Based on Geometric Data Transformations. In Z. Hu, S. Petoukhov, I. Dychka, M. He (Eds), Advances in Computer Science for Engineering and Education. Advances in Intelligent Systems and Computing, 754, 578–587. Cham: Springer.
  30. Teslyuk V., Tsmots I., Tkachenko R., Tkachenko*** P., Rabyk*** V., Opotyak Y., Oliinyk O. Implementation of neuro-like network defuzzifier for mobile platform motion control system // CEUR Workshop Proceedings. – 2024. – Vol. 3664 : Proceedings of the 8th International conference on computational linguistics and intelligent systems. Vol. I: Machine learning workshop, Lviv, Ukraine, April 12–13, 2024. – P. 215–231. https://ceur- ws.org/Vol-3664/paper16.pdf