IKEMOTO Shuhei

写真a

Title

Associate Professor

Laboratory

2-4 Hibikino, Wakamatsu-ku, Kitakyushu-shi, Fukuoka

Research Fields, Keywords

Robotics

Scopus Paper Info  
Total Paper Count: 0  Total Citation Count: 0  h-index: 9

Citation count denotes the number of citations in papers published for a particular year.

Undergraduate Education 【 display / non-display

  • 2005.03   Kanazawa University   Faculty of Engineering   Graduated   JAPAN

  • 2003.03   Toyota National College of Technology     Graduated   JAPAN

Post Graduate Education 【 display / non-display

  • 2010.03  Osaka University  Graduate School, Division of Engineering  Doctoral Program  Completed  JAPAN

Degree 【 display / non-display

  • Osaka University -  Doctor of Engineering  2010.03

Biography in Kyutech 【 display / non-display

  • 2019.04
    -
    Now

    Kyushu Institute of TechnologyGraduate School of Life Science and Systems Engineering   Department of Human Intelligence Systems   Associate Professor  

Biography before Kyutech 【 display / non-display

  • 2015.04
    -
    2019.03

      Assistant Professor   JAPAN

  • 2014.07
    -
    2015.03

      Specially Appointed Assistant Professor   JAPAN

  • 2010.04
    -
    2014.06

      Assistant Professor   JAPAN

 

Publications (Article) 【 display / non-display

  • Noise-modulated neural networks for selectively functionalizing sub-networks by exploiting stochastic resonance

    Ikemoto S.

    Neurocomputing    448   1 - 9   2021.08  [Refereed]

     View Summary

    In the phenomenon of stochastic resonance, adding a certain level of nonzero noise to a nonlinear system reduces information loss. A previous study proposed a neural network consisting of thresholding functions that exploit stochastic resonance at run time and during training, with the aim of smooth mapping and backpropagation. Such a neural network can be rephrased as one that operates only when noise is added, i.e., one that is unable to smoothly map and train when noise is absent. Focusing on both explanations simultaneously, a neural network for which only a sub-network is activated selectively by adding noise locally on that sub-network is proposed in this paper. To this end, a new activation function is introduced. It exploits stochastic resonance and presents null output and derivative when no noise is added. Simple simulations confirm that the proposed neural network with the new activation function allows the sub-network to be functionalized selectively, and interpolations are investigated by imposing varying noise intensity on various regions of the network after sub-networks are trained separately.

    DOI Scopus

  • Neural Model Extraction for Model-Based Control of a Neural Network Forward Model

    Shuhei Ikemoto, Kazuma Takahara, Taiki Kumi, Koh Hosoda

    SN Computer Science      2021.01  [Refereed]

    DOI

  • Autonomous mobile robot for outdoor slope using 2D LiDAR with uniaxial gimbal mechanism

    Hara S., Shimizu T., Konishi M., Yamamura R., Ikemoto S.

    Journal of Robotics and Mechatronics    32 ( 6 ) 1173 - 1182   2020.12  [Refereed]

     View Summary

    © 2020, Fuji Technology Press. All rights reserved. The Nakanoshima Challenge is a contest for developing sophisticated navigation systems of robots for col-lecting garbage in outdoor public spaces. In this study, a robot named Navit(oo)n is designed, and its perfor-mance in public spaces such as city parks is evaluated. Navit(oo)n contains two 2D LiDAR scanners with uniaxial gimbal mechanism, improving self-localization robustness on a slope. The gimbal mechanism adjusts the angle of the LiDAR scanner, preventing erroneous ground detection. We evaluate the navigation perfor-mance of Navit(oo)n in the Nakanoshima and its Extra Challenges.

    repository DOI Scopus

  • Goal-Conditioned Variational Autoencoder Trajectory Primitives with Continuous and Discrete Latent Codes

    Takayuki Osa, Shuhei Ikemoto

    SN Computer Science      2020.09  [Refereed]

    DOI

  • Q-bot: heavy object carriage robot for in-house logistics based on universal vacuum gripper

    Matsuo I., Shimizu T., Nakai Y., Kakimoto M., Sawasaki Y., Mori Y., Sugano T., Ikemoto S., Miyamoto T.

    Advanced Robotics    34 ( 3-4 ) 173 - 188   2020.02  [Refereed]

     View Summary

    © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan. Q-bot is the human-sized carriage robot for lifting heavy weight objects of in-house logistics, such as storehouse and convenience store. The main feature of Q-bot is the adhesion mechanism beneath the foot, called the turnover prevention Universal Vacuum Gripper (in short TP UVG) that holds its body for turnover prevention and self-weight compensation. Turnover prevention is one of the key technologies of in-house logistic robot for effective use of it. Self-weight compensation is another clue for the robot to achieve the labor work in narrow space. TP UVG is achieved both functions by adhering to uneven ground. The other function of Q-bot is multiple objects graspability based on two-sized Universal Vacuum Gripper by dual-armed manipulation. Q-bot also has omnidirectional movability based on mecanum wheels. In this research, we will report on the development of Q-bot and experiments to prevent the robot from falling when it grabs a heavy object while attached to the ground. We also report Q-bot demonstrations of Future Convenience-Store Challenge in the World Robot Summit 2018.

    repository DOI Scopus

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Conference Prsentations (Oral, Poster) 【 display / non-display

  • テンセグリティ構造を利用した連続体マニピュレータ

    塚本 健太,池本 周平

    日本ロボット学会学術講演会  2021.09  -  2021.09  日本ロボット学会

  • ポーラスCNTs-PDMSを用いた触覚センシング

    佐藤 祐亮,アズハリ サマン,田中 啓文,池本 周平

    日本ロボット学会学術講演会  2021.09  -  2021.09  日本ロボット学会

  • 状態方程式を近似するNNからの数式モデル抽出に基づくモデル予測制御

    池本 周平,組 泰樹,細田 耕

    日本ロボット学会学術講演会  2019.09  -  2019.09  日本ロボット学会

Lectures 【 display / non-display

  • NNによってモデル化された運動学・動力学に基づくロボット制御

    第2回IBISML研究会,確率ロボティクスにおける機械学習   2020.10.21 

  • Tactile Sensing based on Time Difference of Pressure Wave Arrival for Inflatable Robots

    IEEE International Conference on Robotics and Automation, Workshop on Unconventional Sensors in Robotics   2020.06.04  IEEE

  • ノイズと創発:確率共鳴による生物規範型の情報処理

    第25回創発システムシンポジウム   2019.09.01 

  • ニューラルネットワークによる状態方程式の近似と制御系設計

    第3回 Honda R&D Co-Research Lab ロボティクス セミナー   2019.07.18