Computational design of ultra-robust strain sensors for soft robot perception and autonomy
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 structura...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-02-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-45786-y |
| _version_ | 1797274119414218752 |
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| author | Haitao Yang Shuo Ding Jiahao Wang Shuo Sun Ruphan Swaminathan Serene Wen Ling Ng Xinglong Pan Ghim Wei Ho |
| author_facet | Haitao Yang Shuo Ding Jiahao Wang Shuo Sun Ruphan Swaminathan Serene Wen Ling Ng Xinglong Pan Ghim Wei Ho |
| author_sort | Haitao Yang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T14:53:48Z |
| format | Article |
| id | doaj.art-b343911fa50844688762633368196601 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-07T14:53:48Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-b343911fa508446887626333681966012024-03-05T19:32:13ZengNature PortfolioNature Communications2041-17232024-02-0115111510.1038/s41467-024-45786-yComputational design of ultra-robust strain sensors for soft robot perception and autonomyHaitao Yang0Shuo Ding1Jiahao Wang2Shuo Sun3Ruphan Swaminathan4Serene Wen Ling Ng5Xinglong Pan6Ghim Wei Ho7Institute of Flexible Electronics (IFE) & Frontiers Science Center for Flexible Electronics, Northwestern Polytechnical UniversityCollege of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and AstronauticsDepartment of Electrical and Computer Engineering, National University of Singapore, SingaporeDepartment of Mechanical Engineering, National University of Singapore, SingaporeDepartment of Computer Science, Columbia UniversityDepartment of Electrical and Computer Engineering, National University of Singapore, SingaporeDepartment of Electrical and Computer Engineering, National University of Singapore, SingaporeDepartment of Electrical and Computer Engineering, National University of Singapore, SingaporeAbstract 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.https://doi.org/10.1038/s41467-024-45786-y |
| spellingShingle | Haitao Yang Shuo Ding Jiahao Wang Shuo Sun Ruphan Swaminathan Serene Wen Ling Ng Xinglong Pan Ghim Wei Ho Computational design of ultra-robust strain sensors for soft robot perception and autonomy Nature Communications |
| title | Computational design of ultra-robust strain sensors for soft robot perception and autonomy |
| title_full | Computational design of ultra-robust strain sensors for soft robot perception and autonomy |
| title_fullStr | Computational design of ultra-robust strain sensors for soft robot perception and autonomy |
| title_full_unstemmed | Computational design of ultra-robust strain sensors for soft robot perception and autonomy |
| title_short | Computational design of ultra-robust strain sensors for soft robot perception and autonomy |
| title_sort | computational design of ultra robust strain sensors for soft robot perception and autonomy |
| url | https://doi.org/10.1038/s41467-024-45786-y |
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