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; Ghim Wei Ho

doi:10.1038/s41467-024-45786-y

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

Nat Commun. 2024 Feb 22;15(1):1636. doi: 10.1038/s41467-024-45786-y.

Authors

Haitao Yang^#^{1

2}, Shuo Ding^#^{3

4}, Jiahao Wang^#², Shuo Sun⁵, Ruphan Swaminathan⁶, Serene Wen Ling Ng², Xinglong Pan², Ghim Wei Ho⁷

Affiliations

¹ Institute of Flexible Electronics (IFE) & Frontiers Science Center for Flexible Electronics, Northwestern Polytechnical University, Xi'an, Shaanxi, 710072, China.
² Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore.
³ College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
⁴ Department of Biomedical Engineering, National University of Singapore, Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore.
⁵ Department of Mechanical Engineering, National University of Singapore, Singapore, 9 Engineering Drive 1, Singapore, 117575, Singapore.
⁶ Department of Computer Science, Columbia University, New York, NY, 10027, USA.
⁷ Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore. elehgw@nus.edu.sg.

^# Contributed equally.

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 (<4% error) and topographical altitude awareness (<10% error). This strategy holds promise for advancing soft robotic capabilities in exploration, rescue operations, and swarming behaviors in complex environments.