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. 336 - 356
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
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