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Soft Particles for Granular Jamming

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Towards Autonomous Robotic Systems (TAROS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11650))

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Abstract

In the last decade, soft robots demonstrated their distinctive advantages compared to ‘hard’ robots. Soft structures can achieve high dexterity and compliance. However, only low forces can be exerted, and more complicated control strategies are needed. Variable stiffness robots offer an alternative solution to compensate for the downsides of flexible robots. One of the most common approach in the development of variable stiffness robots is the use of granular jamming. In this paper a variable stiffness manipulator based on granular jamming is studied. Here, we propose the use of soft and deformable spherical particles instead of commonly used rigid particles. Further on, we evaluate the performance of the soft particles under vacuum. In addition, a comparison between our approach and the standard approach to granular jamming is presented. The proposed soft particles show good performance in terms of their capability of compacting and squeezing against each other to achieve a high-stiffness robot arm.

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Authors and Affiliations

  1. Centre for Advanced Robotics @ Queen Mary, Queen Mary University of London, Mile End Road, London, E1 4NS, UK

    Fabrizio Putzu, Jelizaveta Konstantinova & Kaspar Althoefer

Authors
  1. Fabrizio Putzu
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  2. Jelizaveta Konstantinova
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  3. Kaspar Althoefer
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Corresponding author

Correspondence to Fabrizio Putzu .

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Editors and Affiliations

  1. Queen Mary University of London, London, UK

    Kaspar Althoefer

  2. Queen Mary University of London, London, UK

    Jelizaveta Konstantinova

  3. Queen Mary University of London, London, UK

    Ketao Zhang

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Putzu, F., Konstantinova, J., Althoefer, K. (2019). Soft Particles for Granular Jamming. In: Althoefer, K., Konstantinova, J., Zhang, K. (eds) Towards Autonomous Robotic Systems. TAROS 2019. Lecture Notes in Computer Science(), vol 11650. Springer, Cham. https://doi.org/10.1007/978-3-030-25332-5_6

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  • DOI: https://doi.org/10.1007/978-3-030-25332-5_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-25331-8

  • Online ISBN: 978-3-030-25332-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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