journal article·2021-06

Bio‐Inspired Large‐Area Soft Sensing Skins to Measure UAV Wing Deformation in Flight

Hee‐Sup Shin, Zachary Ott, Leopold G. Beuken, Badri N. Ranganathan, J. Sean Humbert, Sarah Bergbreiter

Advanced Functional Materials· Volume 31· Issue 23
Publisher page

Abstract

Abstract Biological organisms demonstrate remarkable agility in complex environments, especially in comparison to engineered robotic systems. In part, this is due to an organism's ability to detect disturbances and react to them quickly. To address the challenge of quickly sensing these same disturbances in robotic systems, this study proposes and demonstrates large‐area soft sensing skins designed to sense disturbances on unmanned aerial vehicles (UAVs) in flight. These skins are enabled by high‐resolution soft strain sensors embedded into a large‐area skin through a modular molding process that spans feature sizes from tens of microns to 0.675 m. The electronics of the sensing system enable the soft skins to be sampled fast enough to capture dynamic loads on a wing. Overall, the large‐area soft sensing skin demonstrates high sensitivity, mechanical robustness, and consistent sensor readings across static and dynamic tests. The use of the soft sensing skin during UAV flight demonstrates that the sensing skin can capture relevant flight dynamics on small UAVs. These results pave the way to large‐area soft sensing skins for fast and robust control of a wide variety of robotic systems.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Biomimetic flight and propulsion mechanisms · Soft Robotics and Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Mechanical Engineering Carnegie Mellon University Pittsburgh PA 15213 USA

Department of Mechanical Engineering University of Colorado Boulder Boulder CO 80309 USA

Funding

Air Force Office of Scientific Research

References (59)

Show source-provided references
1. 10.1126/scirobotics.aar765010.1126/scirobotics.aar7650 ↗
2. 10.1038/nature1454210.1038/nature14542 ↗
3. 10.1016/j.paerosci.2013.12.00310.1016/j.paerosci.2013.12.003 ↗
4. Sareh S. · 2017 · 6510.1007/978-3-319-46460-2_9 ↗
5. Ramezani A. · Sci. Rob. · 2017 · 2 · 3 Art
6. 10.1126/scirobotics.abc289710.1126/scirobotics.abc2897 ↗
7. L. Castano S. Airoldi T. McKenna J. Humbert 2014 2588
8. 10.1016/j.paerosci.2014.06.00210.1016/j.paerosci.2014.06.002 ↗
9. 10.2514/1.4377810.2514/1.43778 ↗
10. 10.1007/978-3-540-89393-6_610.1007/978-3-540-89393-6_6 ↗
11. 10.1088/1748-3182/8/1/01600410.1088/1748-3182/8/1/016004 ↗
12. 10.1098/rsif.2014.108810.1098/rsif.2014.1088 ↗
13. 10.1242/jeb.10377010.1242/jeb.103770 ↗
14. G. M.Gremillion L. M.Castano J. S.Humbert inAIAA SciTech Conf. 2015.
15. 10.1023/A:100762880062010.1023/A:1007628800620 ↗
16. 10.1023/A:101379023235910.1023/A:1013790232359 ↗
17. 10.1073/pnas.180890911510.1073/pnas.1808909115 ↗
18. 10.2514/1.G00410410.2514/1.G004104 ↗
19. 10.3390/aerospace303001810.3390/aerospace3030018 ↗
20. 10.1016/j.ymssp.2019.10652610.1016/j.ymssp.2019.106526 ↗
21. 10.1007/978-3-642-34738-2_310.1007/978-3-642-34738-2_3 ↗
22. 10.1126/scirobotics.aai752910.1126/scirobotics.aai7529 ↗
23. 10.1021/nl070624410.1021/nl0706244 ↗
24. 10.1126/science.112140110.1126/science.1121401 ↗
25. 10.1002/adma.20060251510.1002/adma.200602515 ↗
26. 10.1021/ma060733p10.1021/ma060733p ↗
27. 10.1002/adfm.20070121610.1002/adfm.200701216 ↗
28. 10.1109/JMEMS.2011.217442110.1109/JMEMS.2011.2174421 ↗
29. 10.1002/admt.20160018810.1002/admt.201600188 ↗
30. 10.1109/JSEN.2012.220079010.1109/JSEN.2012.2200790 ↗
31. 10.1002/adfm.20160273310.1002/adfm.201602733 ↗
32. 10.1038/srep0725410.1038/srep07254 ↗
33. 10.1038/nnano.2011.18410.1038/nnano.2011.184 ↗
34. 10.1002/adma.20140033410.1002/adma.201400334 ↗
35. 10.1002/adfm.20190737510.1002/adfm.201907375 ↗
36. 10.1126/scirobotics.aau691410.1126/scirobotics.aau6914 ↗
37. 10.1109/LRA.2019.289315410.1109/LRA.2019.2893154 ↗
38. 10.1038/s41586-020-2892-610.1038/s41586-020-2892-6 ↗
39. 10.1038/nmat375510.1038/nmat3755 ↗
40. Chossat J.‐B. · 2015 · 2568
41. 10.1002/adfm.20140436510.1002/adfm.201404365 ↗
42. 10.1038/nature1400210.1038/nature14002 ↗
43. 10.1002/adma.20120442610.1002/adma.201204426 ↗
44. 10.1109/JPROC.2019.294136610.1109/JPROC.2019.2941366 ↗
45. 10.1088/0960-1317/24/3/03501710.1088/0960-1317/24/3/035017 ↗
46. PIOCELAN Foam Datasheet http://www.piocelan.com/en/spec/pdf/201512.pdf(accessed: January 2021).
47. H.‐S.Shin A.Charalambides I.Penskiy S.Bergbreiter inIntelligent Robots and Systems (IROS) 2016 IEEE/RSJ International Conference on. IEEE2016 pp.5572–5578.10.1109/IROS.2016.7759819 ↗
48. 10.1063/1.499844010.1063/1.4998440 ↗
49. 10.1126/sciadv.170005310.1126/sciadv.1700053 ↗
50. 10.1038/srep0725410.1038/srep07254 ↗
51. 10.1038/ncomms198010.1038/ncomms1980 ↗
52. 8331 Silver Conductive Epoxy Datasheet https://www.mgchemicals.com/downloads/tds/tds-8331-2parts.pdf(accessed: January 2021).
53. 10.1561/230000005510.1561/2300000055 ↗
54. 10.1038/nature1454310.1038/nature14543 ↗
55. 10.1137/15M103671310.1137/15M1036713 ↗
56. 10.1088/1748-3190/10/5/05601310.1088/1748-3190/10/5/056013 ↗
57. A. I.Weber T. L.Daniel B. W.Brunton bioRxiv 2021 https://doi.org/10.1101/2021.02.09.430476.10.1101/2021.02.09.430476 ↗
58. 10.1088/0964-1726/22/8/08503110.1088/0964-1726/22/8/085031 ↗
59. Weather.gov Wind Gust Definition https://graphical.weather.gov/definitions/defineWindGust.html(accessed: January 2021).