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TLBO-Based Adaptive Neurofuzzy Controller for Mobile Robot Navigation in a Strange Environment.

Awatef Aouf1,2, Lotfi Boussaid2, Anis Sakly3

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This study introduces Teaching-Learning-Based Optimization (TLBO) to train Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for mobile robot navigation. The novel approach optimizes robot paths for efficient navigation in unknown environments.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Mobile robot navigation in unknown environments presents significant challenges.
  • Existing intelligent algorithms have limitations in optimizing trajectory and minimizing travel time.
  • Adaptive Neuro-Fuzzy Inference Systems (ANFIS) offer a flexible framework for complex control problems.

Purpose of the Study:

  • To investigate the efficacy of Teaching-Learning-Based Optimization (TLBO) for training ANFIS parameters.
  • To solve the mobile robot navigation problem in unfamiliar environments.
  • To achieve optimal trajectory planning and minimize travel time for mobile robots.

Main Methods:

  • Employing TLBO to optimize the parameters of an ANFIS structure.
  • Developing a simulation environment to test the navigation strategy.
  • Comparing the performance of the TLBO-based ANFIS with other intelligent algorithms like PSO, IWO, and BBO.

Main Results:

  • The TLBO-based ANFIS demonstrated superior performance in optimizing robot trajectories.
  • The proposed method achieved a significant reduction in travel time compared to benchmark algorithms.
  • Simulations validated the efficiency and effectiveness of the TLBO-ANFIS approach.

Conclusions:

  • TLBO-based ANFIS is a highly efficient and effective method for mobile robot navigation.
  • This approach offers a promising alternative for solving complex navigation tasks in unknown environments.
  • The study highlights the potential of evolutionary optimization techniques in advanced robotics applications.