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Environment Recognition System Based on Multiple Classification Analyses for Mobile Robots

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Abstract

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.

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Authors and Affiliations

  1. Department of Brain Inspired Science and Engineering, Kyushu Institute of Technology, Kitakyushu, 808-0196, Japan

    Atsushi Kanda, Masanori Sato & Kazuo Ishii

Authors
  1. Atsushi Kanda
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  2. Masanori Sato
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  3. Kazuo Ishii
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Correspondence to Atsushi Kanda.

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Kanda, A., Sato, M. & Ishii, K. Environment Recognition System Based on Multiple Classification Analyses for Mobile Robots. J Bionic Eng 5 (Suppl 1), 113–120 (2008). https://doi.org/10.1016/S1672-6529(08)60081-5

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  • DOI: https://doi.org/10.1016/S1672-6529(08)60081-5

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