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Computational design of ultra-robust strain sensors for soft robot perception and autonomy

Author

Listed:
  • Haitao Yang

    (Northwestern Polytechnical University
    National University of Singapore, Singapore)

  • Shuo Ding

    (Nanjing University of Aeronautics and Astronautics
    National University of Singapore, Singapore)

  • Jiahao Wang

    (National University of Singapore, Singapore)

  • Shuo Sun

    (National University of Singapore, Singapore)

  • Ruphan Swaminathan

    (Columbia University)

  • Serene Wen Ling Ng

    (National University of Singapore, Singapore)

  • Xinglong Pan

    (National University of Singapore, Singapore)

  • Ghim Wei Ho

    (National University of Singapore, Singapore)

Abstract

Compliant strain sensors are crucial for soft robots’ perception and autonomy. However, their deformable bodies and dynamic actuation pose challenges in predictive sensor manufacturing and long-term robustness. This necessitates accurate sensor modelling and well-controlled sensor structural changes under strain. Here, we present a computational sensor design featuring a programmed crack array within micro-crumples strategy. By controlling the user-defined structure, the sensing performance becomes highly tunable and can be accurately modelled by physical models. Moreover, they maintain robust responsiveness under various demanding conditions including noise interruptions (50% strain), intermittent cyclic loadings (100,000 cycles), and dynamic frequencies (0–23 Hz), satisfying soft robots of diverse scaling from macro to micro. Finally, machine intelligence is applied to a sensor-integrated origami robot, enabling robotic trajectory prediction (

Suggested Citation

  • Haitao Yang & Shuo Ding & Jiahao Wang & Shuo Sun & Ruphan Swaminathan & Serene Wen Ling Ng & Xinglong Pan & Ghim Wei Ho, 2024. "Computational design of ultra-robust strain sensors for soft robot perception and autonomy," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45786-y
    DOI: 10.1038/s41467-024-45786-y
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    References listed on IDEAS

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