IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v567y2019i7748d10.1038_s41586-019-1022-9.html
   My bibliography  Save this article

Particle robotics based on statistical mechanics of loosely coupled components

Author

Listed:
  • Shuguang Li

    (Massachusetts Institute of Technology
    Columbia University)

  • Richa Batra

    (Columbia University)

  • David Brown

    (Cornell University)

  • Hyun-Dong Chang

    (Cornell University)

  • Nikhil Ranganathan

    (Cornell University)

  • Chuck Hoberman

    (Harvard University
    Harvard University)

  • Daniela Rus

    (Massachusetts Institute of Technology)

  • Hod Lipson

    (Columbia University)

Abstract

Biological organisms achieve robust high-level behaviours by combining and coordinating stochastic low-level components1–3. By contrast, most current robotic systems comprise either monolithic mechanisms4,5 or modular units with coordinated motions6,7. Such robots require explicit control of individual components to perform specific functions, and the failure of one component typically renders the entire robot inoperable. Here we demonstrate a robotic system whose overall behaviour can be successfully controlled by exploiting statistical mechanics phenomena. We achieve this by incorporating many loosely coupled ‘particles’, which are incapable of independent locomotion and do not possess individual identity or addressable position. In the proposed system, each particle is permitted to perform only uniform volumetric oscillations that are phase-modulated by a global signal. Despite the stochastic motion of the robot and lack of direct control of its individual components, we demonstrate physical robots composed of up to two dozen particles and simulated robots with up to 100,000 particles capable of robust locomotion, object transport and phototaxis (movement towards a light stimulus). Locomotion is maintained even when 20 per cent of the particles malfunction. These findings indicate that stochastic systems may offer an alternative approach to more complex and exacting robots via large-scale robust amorphous robotic systems that exhibit deterministic behaviour.

Suggested Citation

  • Shuguang Li & Richa Batra & David Brown & Hyun-Dong Chang & Nikhil Ranganathan & Chuck Hoberman & Daniela Rus & Hod Lipson, 2019. "Particle robotics based on statistical mechanics of loosely coupled components," Nature, Nature, vol. 567(7748), pages 361-365, March.
  • Handle: RePEc:nat:nature:v:567:y:2019:i:7748:d:10.1038_s41586-019-1022-9
    DOI: 10.1038/s41586-019-1022-9
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41586-019-1022-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1038/s41586-019-1022-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marcell Tibor Máthé & Hiroya Nishikawa & Fumito Araoka & Antal Jákli & Péter Salamon, 2024. "Electrically activated ferroelectric nematic microrobots," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    2. Jing Wang & Gao Wang & Huaicheng Chen & Yanping Liu & Peilong Wang & Daming Yuan & Xingyu Ma & Xiangyu Xu & Zhengdong Cheng & Baohua Ji & Mingcheng Yang & Jianwei Shuai & Fangfu Ye & Jin Wang & Yang J, 2024. "Robo-Matter towards reconfigurable multifunctional smart materials," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    3. Gaurav Gardi & Steven Ceron & Wendong Wang & Kirstin Petersen & Metin Sitti, 2022. "Microrobot collectives with reconfigurable morphologies, behaviors, and functions," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    4. Yanbin Li & Antonio Lallo & Junxi Zhu & Yinding Chi & Hao Su & Jie Yin, 2024. "Adaptive hierarchical origami-based metastructures," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    5. Da Zhao & Haobo Luo & Yuxiao Tu & Chongxi Meng & Tin Lun Lam, 2024. "Snail-inspired robotic swarms: a hybrid connector drives collective adaptation in unstructured outdoor environments," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    6. Federico Pratissoli & Andreagiovanni Reina & Yuri Kaszubowski Lopes & Carlo Pinciroli & Genki Miyauchi & Lorenzo Sabattini & Roderich Groß, 2023. "Coherent movement of error-prone individuals through mechanical coupling," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    7. Antoine Aubret & Quentin Martinet & Jeremie Palacci, 2021. "Metamachines of pluripotent colloids," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    8. Li, Wang & Dai, Haifeng & Zhao, Lingzhi & Zhao, Donghua & Sun, Yongzheng, 2023. "Noise-induced consensus of leader-following multi-agent systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 203(C), pages 1-11.
    9. Alexander Ziepke & Ivan Maryshev & Igor S. Aranson & Erwin Frey, 2022. "Multi-scale organization in communicating active matter," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    10. Zhou, Yongjian & Wang, Tonghao & Lei, Xiaokang & Peng, Xingguang, 2024. "Collective behavior of self-propelled particles with heterogeneity in both dynamics and delays," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:nature:v:567:y:2019:i:7748:d:10.1038_s41586-019-1022-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.