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SLUGBOT, an Aplysia-Inspired Robotic Grasper for Studying Control

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Biomimetic and Biohybrid Systems (Living Machines 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13548))

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Abstract

Living systems can use a single periphery to perform a variety of tasks and adapt to a dynamic environment. This multifunctionality is achieved through the use of neural circuitry that adaptively controls the reconfigurable musculature. Current robotic systems struggle to flexibly adapt to unstructured environments. Through mimicry of the neuromechanical coupling seen in living organisms, robotic systems could potentially achieve greater autonomy. The tractable neuromechanics of the sea slug Aplysia californica’s feeding apparatus, or buccal mass, make it an ideal candidate for applying neuromechanical principles to the control of a soft robot. In this work, a robotic grasper was designed to mimic specific morphology of the Aplysia feeding apparatus. These include the use of soft actuators akin to biological muscle, a deformable grasping surface, and a similar muscular architecture. A previously developed Boolean neural controller was then adapted for the control of this soft robotic system. The robot was capable of qualitatively replicating swallowing behavior by cyclically ingesting a plastic tube. The robot’s normalized translational and rotational kinematics of the odontophore followed profiles observed in vivo despite morphological differences. This brings Aplysia-inspired control in roboto one step closer to multifunctional neural control schema in vivo and in silico. Future additions may improve SLUGBOT’s viability as a neuromechanical research platform.

K. Dai, R. Sukhnandan, M. Bennington—These authors contributed equally to the work.

This work was supported by NSF DBI2015317 as part of the NSF/CIHR/DFG/FRQ/UKRI-MRC Next Generation Networks for Neuroscience Program and by the NSF Research Fellowship Program under Grant No. DGE1745016. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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Acknowledgment

We thank Jesse Grupper (Harvard University) and Al Turney (KOGANEI International America, Inc.) for help in developing the pneumatic controller.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Kevin Dai, Michael Bennington, Karen Whirley, Ryan Bao & Victoria A. Webster-Wood

  2. Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Ravesh Sukhnandan & Victoria A. Webster-Wood

  3. McGowan Institute for Regenerative Medicine, Carnegie Mellon University, Pittsburgh, PA, USA

    Victoria A. Webster-Wood

  4. Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA

    Lu Li

  5. Department of Biology, Case Western Reserve University, Cleveland, OH, USA

    Jeffrey P. Gill & Hillel J. Chiel

  6. Department of Neurosciences, Case Western Reserve University, Cleveland, OH, USA

    Hillel J. Chiel

  7. Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA

    Hillel J. Chiel

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  1. Kevin Dai
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  8. Hillel J. Chiel
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Corresponding author

Correspondence to Victoria A. Webster-Wood .

Editor information

Editors and Affiliations

  1. Portland State University, Portland, OR, USA

    Alexander Hunt

  2. Technology Innovation Institute, Masdar City, United Arab Emirates

    Vasiliki Vouloutsi

  3. Case Western Reserve University, Cleveland, OH, USA

    Kenneth Moses

  4. Case Western Reserve University, Cleveland, OH, USA

    Roger Quinn

  5. Institute for Bioengineering of Cataloni, Barcelona, Spain

    Anna Mura

  6. University of Sheffield, Sheffield, UK

    Tony Prescott

  7. Radboud University, Nijmegen, The Netherlands

    Paul F. M. J. Verschure

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Dai, K. et al. (2022). SLUGBOT, an Aplysia-Inspired Robotic Grasper for Studying Control. In: Hunt, A., et al. Biomimetic and Biohybrid Systems. Living Machines 2022. Lecture Notes in Computer Science(), vol 13548. Springer, Cham. https://doi.org/10.1007/978-3-031-20470-8_19

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  • DOI: https://doi.org/10.1007/978-3-031-20470-8_19

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