Journal of Bionic Engineering

, Volume 5, Supplement 1, pp 113–120 | Cite as

Environment Recognition System Based on Multiple Classification Analyses for Mobile Robots

  • Atsushi KandaEmail author
  • Masanori Sato
  • Kazuo Ishii


Various mechanisms have recently been developed that combine linkage mechanisms and wheels. In particular, the combination of passive linkage mechanisms and small wheels is a main research trend because standard wheeled mobile mechanisms find it difficult to move on rough terrain. In our previous research, a six-wheel mobile robot employing a passive linkage mechanism has been developed to enhance maneuverability and was able to climb over a 0.20 m bump and stairs. We designed a hybrid velocity and torque controller using a neural network since simple velocity controllers fail to climb up. In this paper, we propose an environment recognition system for a wheeled mobile robot that consists of multiple classification analyses to make the robot more adaptive to various environments by selecting a suitable system such as decision making, navigation and controller using the result of the environment recognition system. We evaluate the recognition performance in operation environments; slopes, bumps and stairs by comparing principle component, k-means and self-organizing map analyses.


wheeled mobile robot neural network self-organizing map environment recognition 


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  1. [1]
    Volpe R, Balaram J, Ohm T, Ivlev R. The Rocky 7 Mars rover prototype. Proceedings of the 1996 IEEE/RSJ International Conference on Intelligent Robots and Systems, 1996, 3, 1558–1564.Google Scholar
  2. [2]
    Kuroda Y, Teshima T, Sato Y, Kubota T. Mobility performance evaluation of planetary rover with similarity model experiment. Proceedings of the 2004 IEEE International Conference on Robotics and Automation, 2004, 2, 2098–2103.CrossRefGoogle Scholar
  3. [3]
    Estier T, Crausaz Y, Merminod B, Lauria M, Piguet R, Siegwart R. An innovative space rover with extended climbing abilities. Proceedings of the Space and Robotics 2000, New Mexico, USA, 2000, 333–339.CrossRefGoogle Scholar
  4. [4]
    Thueer T, Lamon P, Krebs A, Siegwart R. CRAB-exploration rover with advanced obstacle negotiation capabilities. Proceedings of the 9th ESA Workshop on Advanced Space Technologies for Robotics and Automation, Nordwijk, The Netherlands, 2006, 1–8.Google Scholar
  5. [5]
    Chugo D, Kawabata K, Kaetsu H, Asama H, Mishima T. Development of a control system for an omni-directional vehicle with step-climbing ability. Advanced Robotics, 2005, 19, 55–71.CrossRefGoogle Scholar
  6. [6]
    Miura J, Negishi Y, Shirai Y. Map generation of a mobile robot by integrating omni-directional stereo and laser range finder. JRSJ, 2003, 21, 110–116. (in Japanese)CrossRefGoogle Scholar
  7. [7]
    Sato M, Ishii K. A neural network based controller for a wheel type mobile robot. 2nd International Conference on Brain-Inspired Information Technology, 2006, 1291, 261–264.Google Scholar
  8. [8]
    Wataru S, Yoshitaka G, Kazuyuki K, Kajiro W. Application of particle filter to autonoumous navigation system for outdoor environment. SICE Annual Conference 2008, Chofu, Tokyo, Japan, 2008, 93–96.CrossRefGoogle Scholar
  9. [9]
    Jia S, Yasuda A, Chugo D, Takase K. LRF-based self-localization of mobile robot using extended kalman filter. SICE Annual Conference 2008, Chofu, Tokyo, Japan, 2008, 2295–2298.Google Scholar
  10. [10]
    Sasaki H, Kubota N, Taniguchi K. Growing topological map for SLAM of mobile robots. SICE Annual Conference 2008, Chofu, Tokyo, Japan, 2008, 3523–3528.CrossRefGoogle Scholar
  11. [11]
    Kohonen T. Self-Organizing Maps, Springer-Verlag, Berlin, Germany, 2001.CrossRefGoogle Scholar
  12. [12]
    Richard O D, Pater E H, Davit G S. Pattern Classification, second edition, John Wiley and Sons, New York, 2007.Google Scholar
  13. [13]
    Provost J, Kuipers B J, Miikkulainen R. Self-organizing perceptual and temporal abstraction for robot reinforcement learning. AAAI Workshop on Learning and Planning in Markov Processes, 2004.Google Scholar
  14. [14]
    Fujii T, Hayashi A, Hida H. Using principle component analysis and non-hierarchical clustering to find landmarks. The 48th Annual Conference of the IPSJ, 1994, 53–54. (in Japanese)Google Scholar
  15. [15]
    Saegusa R, Sakano H, Hashimoto S. A nonlinear principle component anaysis of image data. IEICE Transactions on Information and Systems, 2005, E88-D, 2242–2248.CrossRefGoogle Scholar
  16. [16]
    Takeshi Y, Shun Y, Kazunori K, Tesuya O, Hiroshi O. Speech recognition of using lip images from various directions. The 18th Annual Conference of the Japanese Society for Artificial Intelligence, 2004, 18, page no. 1E2-02. (in Japanese)Google Scholar
  17. [17]
    Sato M, Kanda A, Ishii K. A neural network controller designed for a rough terrain mobile robot 2nd report: Effects on the controller property caused by the process of teaching data generation. Proceeding of the 2007 JSME Conference on Robotics and Mechatronics, Akita, Japan, 2007, page no. 2A1-D10. (in Japanese)Google Scholar
  18. [18]
    Sato M, Kanda A, Ishii K. A switching controller system for a wheeled mobile robot. Journal of Bionic Engineering, 2007, 4, 281–289.CrossRefGoogle Scholar
  19. [19]
    Furukawa T. SOM of SOMs: Self-organizing map which maps a group of self-organizing maps. Lecture Notes in Computer Science, 2005, 3696, 391–396.CrossRefGoogle Scholar
  20. [20]
    Tenenbaum J B, de Silva V, Langford J C. A global geometric framework for nonlinear dimensionality reduction. Science, 2000, 290, 2319–2323.CrossRefGoogle Scholar

Copyright information

© Jilin University 2008

Authors and Affiliations

  1. 1.Department of Brain Inspired Science and EngineeringKyushu Institute of TechnologyKitakyushuJapan

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