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A fuzzy controller with supervised learning assisted reinforcement learning algorithm for obstacle avoidance.

Cang Ye1, N C Yung, Danwei Wang

  • 1Adv. Technol. Lab., Univ. of Michigan, Ann Arbor, MI, USA.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 2, 2008
PubMed
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This study introduces a two-phase neural fuzzy system for mobile robot obstacle avoidance. It uses supervised and reinforcement learning in a virtual environment for efficient, collision-free navigation.

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Fuzzy logic systems offer efficient obstacle avoidance but struggle with human expert-based rule base maintenance.
  • Reinforcement learning can automate fuzzy rule learning but faces challenges with extensive training and the curse of dimensionality.

Purpose of the Study:

  • To propose a novel neural fuzzy system that overcomes limitations of traditional fuzzy logic and reinforcement learning for robot navigation.
  • To develop an efficient two-phase learning approach combining supervised and reinforcement learning for robust obstacle avoidance.

Main Methods:

  • A mixed coarse and fine learning strategy is employed: supervised learning for initial membership function determination, followed by reinforcement learning for fine-tuning.
  • A modified Sutton and Barto reinforcement learning model is utilized to enhance exploration for more effective learning.

Related Experiment Videos

  • A virtual environment (VE) simulator, offering desktop (DVE) and immersive (IVE) visualization, is developed to generate consistent training data.

    • Main Results:

    • The proposed neural fuzzy system enables mobile robots to achieve collision-free navigation through a two-step tuning process.
    • The virtual environment facilitates the acquisition of large, consistent datasets, mitigating challenges associated with real-world data collection.
    • The enhanced exploration in the learning algorithm contributes to a more sufficiently learned and effective fuzzy rule base.

    Conclusions:

    • The developed neural fuzzy system provides an effective solution for autonomous mobile robot navigation and obstacle avoidance.
    • The integration of supervised and reinforcement learning, coupled with a VE simulator, offers a scalable and efficient approach to training intelligent robotic systems.