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An Autonomous Mobile Robot with Functions of Action Learning, Memorizing, Recall and Identifying the Environment Using Gaussian Mixture Model

  • Masanao ObayashiEmail author
  • Taiki Yamane
  • Takashi Kuremoto
  • Shingo Mabu
  • Kunikazu Kobayashi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9489)

Abstract

In this paper, behavior scheme of autonomous mobile robots to achieve the objectives of them in environments are proposed, having function of identifying the current environment in which they are placed and making use of learning, memorizing and recalling behaviors of corresponding to each of plural different environments. Specifically, each robot has the function of identifying the environment using some behavioral statistical data for each environment, and if the robot has already experienced the environment, it behaves by making use of own experienced data stored in the database, otherwise it performs a new behavior learning and adds the learning results into the database.

Keywords

Intelligent robot Chaotic neural network Reinforcement learning Identification of the environment Gaussian mixture model 

References

  1. 1.
    Obayashi, M., Narita, K., Kuremoto, T., Kobayashi, K.: A reinforcement learning system with chaotic neural networks-based adaptive hierarchical memory structure for autonomous robots. In: Proceedings of International Conference on Control, Automation and Systems 2008 (ICCAS 2008), pp. 69–74 (2008)Google Scholar
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    Obayashi, M., Narita, K., Okamoto, Y., Kuremoto, T., Kobayashi, K., Feng, L.: A reinforcement learning system embedded agent with neural network-based adaptive hierarchical memory structure. In: Advances in Reinforcement Learning, chapter 11, pp.189–208, IN-TECH (2011)Google Scholar
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    Adachi, M., Aihara, K.: Associative dynamics in a chaotic neural network. Neural Netw. 10(1), 83–98 (1997)CrossRefGoogle Scholar
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    Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT PRESS, Cambridge (1988)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Masanao Obayashi
    • 1
    Email author
  • Taiki Yamane
    • 1
  • Takashi Kuremoto
    • 1
  • Shingo Mabu
    • 1
  • Kunikazu Kobayashi
    • 2
  1. 1.Yamaguchi UniversityUbe, YamaguchiJapan
  2. 2.Aichi Prefectural UniversityNagakute, AichiJapan

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