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Obstacle Avoidance of Mobile Robot Using Reinforcement Learning in Virtual Environment

  • Journal of Internet of Things and Convergence
  • Abbr : JKIOTS
  • 2021, 7(4), pp.29-34
  • DOI : 10.20465/KIOTS.2021.7.4.029
  • Publisher : The Korea Internet of Things Society
  • Research Area : Engineering > Computer Science > Internet Information Processing
  • Received : October 18, 2021
  • Accepted : December 5, 2021
  • Published : December 31, 2021

LEEJONGLAK 1

1영남이공대학교

Accredited

ABSTRACT

In order to apply reinforcement learning to a robot in a real environment, it is necessary to use simulation in a virtual environment because numerous iterative learning is required. In addition, it is difficult to apply a learning algorithm that requires a lot of computation for a robot with low-spec. hardware. In this study, ML-Agent, a reinforcement learning frame provided by Unity, was used as a virtual simulation environment to apply reinforcement learning to the obstacle collision avoidance problem of mobile robots with low-spec hardware. A DQN supported by ML-Agent is adopted as a reinforcement learning algorithm and the results for a real robot show that the number of collisions occurred less then 2 times per minute.

Citation status

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