Letter

Small-scale soft-bodied robot with multimodal locomotion

  • Nature volume 554, pages 8185 (01 February 2018)
  • doi:10.1038/nature25443
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Abstract

Untethered small-scale (from several millimetres down to a few micrometres in all dimensions) robots that can non-invasively access confined, enclosed spaces may enable applications in microfactories such as the construction of tissue scaffolds by robotic assembly1, in bioengineering such as single-cell manipulation and biosensing2, and in healthcare3,4,5,6 such as targeted drug delivery4 and minimally invasive surgery3,5. Existing small-scale robots, however, have very limited mobility because they are unable to negotiate obstacles and changes in texture or material in unstructured environments7,8,9,10,11,12,13. Of these small-scale robots, soft robots have greater potential to realize high mobility via multimodal locomotion, because such machines have higher degrees of freedom than their rigid counterparts14,15,16. Here we demonstrate magneto-elastic soft millimetre-scale robots that can swim inside and on the surface of liquids, climb liquid menisci, roll and walk on solid surfaces, jump over obstacles, and crawl within narrow tunnels. These robots can transit reversibly between different liquid and solid terrains, as well as switch between locomotive modes. They can additionally execute pick-and-place and cargo-release tasks. We also present theoretical models to explain how the robots move. Like the large-scale robots that can be used to study locomotion17, these soft small-scale robots could be used to study soft-bodied locomotion produced by small organisms.

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Acknowledgements

W.H. thanks the Alexander von Humboldt Foundation for financial support. This work is funded by the Max Planck Society. We thank Z. Burghard and A. Diem from the University of Stuttgart for evaluating the Young’s modulus of our robots, K. Suppelt and S. Meyer from Fujifilm Visualsonics for their help with the ultrasound-guided experiments, and the members from Physical Intelligence Department at the Max Planck Institute for Intelligent Systems for their comments.

Author information

Author notes

    • Wenqi Hu
    •  & Guo Zhan Lum

    These authors contributed equally to this work.

Affiliations

  1. Physical Intelligence Department, Max Planck Institute for Intelligent Systems, 70569 Stuttgart, Germany

    • Wenqi Hu
    • , Guo Zhan Lum
    • , Massimo Mastrangeli
    •  & Metin Sitti

Authors

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Contributions

M.S., W.H., G.Z.L. and M.M. proposed and designed the research. W.H. performed all experiments. G.Z.L. developed all theoretical and empirical models, except for the meniscus-climbing model, which was developed by M.M. The experimental data were analysed by W.H., G.Z.L. and M.M. All authors wrote the paper and participated in discussions.

Competing interests

Max Planck Innovation filed a provisional patent application on behalf of all authors (PCT/EP2017/084408) based on the methods and results presented here.

Corresponding author

Correspondence to Metin Sitti.

Reviewer Information Nature thanks K.-J. Cho, R. Kramer-Bottiglio and B. Mazzolai for their contribution to the peer review of this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    This file contains supplementary data S1-S15, figures S1-S45 and S1-S4.

Videos

  1. 1.

    Jellyfish-like swimming

    The video sequentially shows jellyfish-like swimming in slow motion (Fig. 2a), visualization of the fluid vortices produced by the jellyfish-like swimming locomotion as traced by 45 μm beads (Fig. S37), and arrest of the jellyfish-like swimming locomotion when B flipping is stopped.

  2. 2.

    Meniscus climbing and landing

    The video sequentially shows the robot climbing a water meniscus (Fig. 2b) and landing on a solid platform (Fig. 2c).

  3. 3.

    Rolling and walking

    The video sequentially presents rolling (Fig. 2e) and straight walking (Fig. 2f), demonstrates steered walking, and a comparison of using rolling or walking to cross a gap.

  4. 4.

    Crawling

    The video presents the relationship between the traveling wave produced on the soft robot body and the crawling direction (Fig. 2g), and demonstrates that the robot’s crawling direction can be flipped by reversing the direction of the traveling wave.

  5. 5.

    Jumping

    The video first presents the directional jumping locomotion shown in Fig. 2h. Subsequently, it presents the straight jumping locomotion, which is induced solely via the shapechange mechanism. It further shows how the straight jumping locomotion can be affected by different vertical magnetic field spatial gradients. Finally, it presents a control experiment in which a robot that has a homogenous magnetization profile is unable to jump, as opposed to a robot with a harmonic magnetization profile.

  6. 6.

    Multimodal locomotion

    The video presents the sequence of Fig. 3, whereby the soft robot navigates through different terrains by combining all the discussed locomotion modes.

  7. 7.

    Multimodal locomotion in a surgical phantom

    The video presents the soft robot navigating through a stomach phantom by a combination of meniscus climbing, landing, rolling and jumping, also shown in Fig. 4a. In the video, the robot moves very quickly at around 00:34 because it is pulled by unwanted magnetic gradient-based pulling forces generated by the spatial gradients of B.

  8. 8.

    Ultrasound-guided locomotion

    The video shows ex-vivo ultrasound-guided locomotion of the soft robot (Fig. 4b and Fig. S44). Jellyfish-like swimming, rolling and crawling are respectively demonstrated in three different biological phantoms.

  9. 9.

    Cargo transport

    The video demonstrates gripping, transportation and release of a cargo by the soft robot (Fig. 4c).

  10. 10.

    Cargo delivery

    The video demonstrates selectively triggered cargo release by a modified soft robot (Fig. 4d and Fig. S45).

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