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

Haitao Yang, Shuo Ding, Jiahao Wang, Shuo Sun, Ruphan Swaminathan, Serene Wen Ling Ng, Xinglong Pan and Ghim Wei Ho ()
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Haitao Yang: Northwestern Polytechnical University
Shuo Ding: Nanjing University of Aeronautics and Astronautics
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

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: 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 (

Date: 2024
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DOI: 10.1038/s41467-024-45786-y

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