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KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications

KES-AMSTA 2017: Agent and Multi-Agent Systems: Technology and Applications pp 200–209Cite as

Towards Robot Fall Detection and Management for Russian Humanoid AR-601

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Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 74))

Abstract

While interacting in a human environment, a fall is the main threat to safety and successful operation of humanoid robots, and thus it is critical to explore ways to detect and manage an unavoidable fall of humanoid robots. Even assuming perfect bipedal walking strategies and algorithms, there exist several unexpected factors, which can threaten existing balance of a humanoid robot. These include such issues as power failure, robot component failures, communication disruptions and failures, sudden forces applied to the robot externally as well as internally generated exceed torques etc. As progress in a humanoid robotics continues, robots attain more autonomy and enter realistic human environments, they will inevitably encounter such factors more frequently. Undesirable fall might cause serious physical damage to a human user, to a robot and to surrounding environment. In this paper, we present a brief review of strategies that include algorithms for fall prediction, avoidance, and damage control of small-size and human-size humanoids, which will be further implemented for Russian humanoid robot AR-601.

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Acknowledgments

Part of the work was performed according to the Russian Government Program of Competitive Growth of Kazan Federal University.

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Authors and Affiliations

  1. Intelligent Robotic Systems Laboratory, Higher Institute for Information Technology and Information Systems (ITIS), Kazan Federal University, Kazan, Russian Federation

    Evgeni Magid & Artur Sagitov

Authors
  1. Evgeni Magid
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  2. Artur Sagitov
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Correspondence to Artur Sagitov .

Editor information

Editors and Affiliations

  1. Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia

    Gordan Jezic

  2. Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia

    Mario Kusek

  3. School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, United Kingdom

    Yun-Heh Jessica Chen-Burger

  4. Fern Barrow, Bournemouth University, Poole, Dorset, United Kingdom

    Robert J. Howlett

  5. University of Canberra, Canberra, Aust Capital Terr, Australia

    Lakhmi C. Jain

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Magid, E., Sagitov, A. (2018). Towards Robot Fall Detection and Management for Russian Humanoid AR-601. In: Jezic, G., Kusek, M., Chen-Burger, YH., Howlett, R., Jain, L. (eds) Agent and Multi-Agent Systems: Technology and Applications. KES-AMSTA 2017. Smart Innovation, Systems and Technologies, vol 74. Springer, Cham. https://doi.org/10.1007/978-3-319-59394-4_20

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  • DOI: https://doi.org/10.1007/978-3-319-59394-4_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59393-7

  • Online ISBN: 978-3-319-59394-4

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