Path Planning for Mobile Robots Using an Improved Reinforcement Learning Scheme

DOI

Description

The current method for establishing travel routes provides modeled environmental information. However, it is difficult to create an environment model for the environments in which mobile robot travel because the environment changes constantly due to the existence of moving objects, including pedestrians. In this study, we propose a path planning system for mobile robots using reinforcement-learning systems and Cerebellar Model Articulation Controllers (CMACs). We selected the best travel route utilizing these reinforcement-learning systems. When a CMAC learns the value function of Q-Learning, it improves learning speed by utilizing the generalizing action. CMACs enable us to reduce the time needed to select the best travel route. Using simulation and real robots, we performed a path-planning experiment. We report the results of simulation and experiment on travelling by on-line learning.

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Details 詳細情報について

  • CRID
    1390282680562270464
  • NII Article ID
    130006960450
  • DOI
    10.11499/sicep.2002.0.481.0
  • Text Lang
    en
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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