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Mergeable nervous systems for robots

Author

Listed:
  • Nithin Mathews

    (IRIDIA, CoDE, Université Libre de Bruxelles)

  • Anders Lyhne Christensen

    (Instituto Universitário de Lisboa (ISCTE-IUL), Instituto de Telecomunicações)

  • Rehan O’Grady

    (IRIDIA, CoDE, Université Libre de Bruxelles)

  • Francesco Mondada

    (Institut de Microinformatique (IMT), Faculté des Sciences et Technique de l’Ingénieur (STI), Ecole polytechnique fédérale de Lausanne)

  • Marco Dorigo

    (IRIDIA, CoDE, Université Libre de Bruxelles)

Abstract

Robots have the potential to display a higher degree of lifetime morphological adaptation than natural organisms. By adopting a modular approach, robots with different capabilities, shapes, and sizes could, in theory, construct and reconfigure themselves as required. However, current modular robots have only been able to display a limited range of hardwired behaviors because they rely solely on distributed control. Here, we present robots whose bodies and control systems can merge to form entirely new robots that retain full sensorimotor control. Our control paradigm enables robots to exhibit properties that go beyond those of any existing machine or of any biological organism: the robots we present can merge to form larger bodies with a single centralized controller, split into separate bodies with independent controllers, and self-heal by removing or replacing malfunctioning body parts. This work takes us closer to robots that can autonomously change their size, form and function.

Suggested Citation

  • Nithin Mathews & Anders Lyhne Christensen & Rehan O’Grady & Francesco Mondada & Marco Dorigo, 2017. "Mergeable nervous systems for robots," Nature Communications, Nature, vol. 8(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-00109-2
    DOI: 10.1038/s41467-017-00109-2
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    Cited by:

    1. Xiong Yang & Rong Tan & Haojian Lu & Toshio Fukuda & Yajing Shen, 2022. "Milli-scale cellular robots that can reconfigure morphologies and behaviors simultaneously," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    2. 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.
    3. 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.

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