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Fuzzy Logic Based Control for Autonomous Mobile Robot Navigation.

Hajer Omrane1, Mohamed Slim Masmoudi1, Mohamed Masmoudi1

  • 1Department of Electrical Engineering, METS Laboratory, National School of Engineers of Sfax, University of Sfax, Street of Soukra Km. 4, BP 1173, 3038 Sfax, Tunisia.

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This study introduces a single fuzzy logic controller for mobile robot navigation and obstacle avoidance in indoor settings. This approach simplifies control systems, improving trajectory tracking performance.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Mobile robot navigation in indoor environments presents challenges, often requiring separate systems for path planning and obstacle avoidance.
  • Existing methods frequently employ multiple independent controllers, increasing system complexity.

Purpose of the Study:

  • To design and implement a unified fuzzy logic controller for mobile robot trajectory tracking and simultaneous obstacle avoidance.
  • To demonstrate the efficacy of a single fuzzy controller in managing both navigation and safety in indoor environments.

Main Methods:

  • A mobile robot equipped with DC motors, infrared (IR) sensors for obstacle detection, and optical encoders for position feedback was utilized.
  • A fuzzy logic controller was developed to integrate navigation and obstacle avoidance functionalities.
  • Simulations were conducted using MATLAB and the SIMIAM navigation platform to assess performance.

Main Results:

  • The integrated fuzzy controller successfully enabled the mobile robot to track various trajectories while avoiding obstacles.
  • Performance was evaluated based on simulation time and the length of the travelled path, indicating efficient navigation.
  • The single fuzzy controller approach proved effective in managing complex indoor navigation tasks.

Conclusions:

  • A single fuzzy logic controller can effectively manage both trajectory tracking and obstacle avoidance for mobile robots in indoor environments.
  • This integrated approach offers a simplified and efficient solution compared to traditional multi-controller systems.
  • The simulation results validate the robust performance of the proposed intelligent navigation algorithm.