Intelligent Recognition Using Ultralight Multifunctional Nano-Layered Carbon Aerogel Sensors with Human-Like Tactile Perception
Corresponding Author: Ya Yang
Nano-Micro Letters,
Vol. 16 (2024), Article Number: 11
Abstract
Humans can perceive our complex world through multi-sensory fusion. Under limited visual conditions, people can sense a variety of tactile signals to identify objects accurately and rapidly. However, replicating this unique capability in robots remains a significant challenge. Here, we present a new form of ultralight multifunctional tactile nano-layered carbon aerogel sensor that provides pressure, temperature, material recognition and 3D location capabilities, which is combined with multimodal supervised learning algorithms for object recognition. The sensor exhibits human-like pressure (0.04–100 kPa) and temperature (21.5–66.2 °C) detection, millisecond response times (11 ms), a pressure sensitivity of 92.22 kPa−1 and triboelectric durability of over 6000 cycles. The devised algorithm has universality and can accommodate a range of application scenarios. The tactile system can identify common foods in a kitchen scene with 94.63% accuracy and explore the topographic and geomorphic features of a Mars scene with 100% accuracy. This sensing approach empowers robots with versatile tactile perception to advance future society toward heightened sensing, recognition and intelligence.
Highlights:
1 individual sensor can provide multiple tactile sensations: pressure, temperature, materials recognition, and 3D location. ThereforThe tactile performance of ultralight multifunctional sensors can reach the level of human tactile perception.
2 An e, it is no longer necessary to integrate multiple sensing modules with different functions, which greatly simplifies system complexity and reduces energy loss.
3 The tactile system with multimodal learning algorithms has universality and can accommodate object recognition tasks in various application scenarios (e.g., Mars and Kitchen).
Keywords
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References
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N. Fazeli, M. Oller, J. Wu, Z. Wu, J.B. Tenenbaum, A. Rodriguez, See, feel, act: Hierarchical learning for complex manipulation skills with multisensory fusion. Sci. Robot. 4, eaav3123 (2019). https://doi.org/10.1126/scirobotics.aav3123
J. Pesnot Lerousseau, C.V. Parise, M.O. Ernst, V. van Wassenhove, Multisensory correlation computations in the human brain identified by a time-resolved encoding model. Nat. Commun. 13, 2489 (2022). https://doi.org/10.1038/s41467-022-29687-6
H. Tan, Y. Zhou, Q. Tao, J. Rosen, S. van Dijken, Bioinspired multisensory neural network with crossmodal integration and recognition. Nat. Commun. 12, 1120 (2021). https://doi.org/10.1038/s41467-021-21404-z
A. Billard, D. Kragic, Trends and challenges in robot manipulation. Science 364, eaat8414 (2019). https://doi.org/10.1126/science.aat8414
H. Sun, K.J. Kuchenbecker, G. Martius, A soft thumb-sized vision-based sensor with accurate all-round force perception. Nat. Mach. Intell. 4, 135–145 (2022). https://doi.org/10.1038/s42256-021-00439-3
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Y. Gao, C. Yan, H. Huang, T. Yang, G. Tian et al., Microchannel-confined MXene based flexible piezoresistive multifunctional micro-force sensor. Adv. Funct. Mater. 30, 1909603 (2020). https://doi.org/10.1002/adfm.201909603
B. Wang, X. Lai, H. Li, C. Jiang, J. Gao et al., Multifunctional MXene/chitosan-coated cotton fabric for intelligent fire protection. ACS Appl. Mater. Interfaces 13, 23020–23029 (2021). https://doi.org/10.1021/acsami.1c05222
L. Groo, D.J. Inman, H.A. Sodano, In situ damage detection for fiber-reinforced composites using integrated zinc oxide nanowires. Adv. Funct. Mater. 28, 1802846 (2018). https://doi.org/10.1002/adfm.201802846
J. Wen, J. Tang, H. Ning, N. Hu, Y. Zhu et al., Multifunctional ionic skin with sensing, UV-filtering, water-retaining, and anti-freezing capabilities. Adv. Funct. Mater. 31, 2011176 (2021). https://doi.org/10.1002/adfm.202011176
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C. Leovy, Weather and climate on Mars. Nature 412, 245–249 (2001). https://doi.org/10.1038/35084192
L.K. Fenton, P.E. Geissler, R.M. Haberle, Global warming and climate forcing by recent albedo changes on Mars. Nature 446, 646–649 (2007). https://doi.org/10.1038/nature05718
A.S. Yen, R. Gellert, C. Schröder, R.V. Morris, J.F. Bell et al., An integrated view of the chemistry and mineralogy of martian soils. Nature 436, 49–54 (2005). https://doi.org/10.1038/nature03637
R. Rieder, T. Economou, H. Wänke, A. Turkevich, J. Crisp et al., The chemical composition of martian soil and rocks returned by the mobile alpha proton X-ray spectrometer: preliminary results from the X-ray mode. Science 278, 1771–1774 (1997). https://doi.org/10.1126/science.278.5344.1771
D. Clery, Lake spied deep below polar ice cap on Mars. Science 361, 320–320 (2018). https://doi.org/10.1126/science.361.6400.320
A. Diez, Liquid water on Mars. Science 361, 448–449 (2018). https://doi.org/10.1126/science.aau1829
R. Orosei, S.E. Lauro, E. Pettinelli, A. Cicchetti, M. Coradini et al., Radar evidence of subglacial liquid water on Mars. Science 361, 490–493 (2018). https://doi.org/10.1126/science.aar7268
S.E. Lauro, E. Pettinelli, G. Caprarelli, L. Guallini, A.P. Rossi et al., Multiple subglacial water bodies below the south pole of Mars unveiled by new MARSIS data. Nat. Astron. 5, 63–70 (2021). https://doi.org/10.1038/s41550-020-1200-6
Y. Liu, X. Wu, Y.-Y.S. Zhao, L. Pan, C. Wang et al., Zhurong reveals recent aqueous activities in Utopia Planitia. Mars. Sci. Adv. 8, eabn8555 (2022). https://doi.org/10.1126/sciadv.abn8555
H. Zhuo, Y. Hu, X. Tong, Z. Chen, L. Zhong et al., A supercompressible, elastic, and bendable carbon aerogel with ultrasensitive detection limits for compression strain, pressure, and bending angle. Adv. Mater. 30, 1706705 (2018). https://doi.org/10.1002/adma.201706705
Z. Sun, M. Zhu, Z. Zhang, Z. Chen, Q. Shi et al., Artificial intelligence of things (AIoT) enabled virtual shop applications using self-powered sensor enhanced soft robotic manipulator. Adv. Sci. 8, 2100230 (2021). https://doi.org/10.1002/advs.202100230
X. Qu, Z. Liu, P. Tan, C. Wang, Y. Liu et al., Artificial tactile perception smart finger for material identification based on triboelectric sensing. Sci. Adv. 8, eabq2521 (2022). https://doi.org/10.1126/sciadv.abq2521
H. Zhuo, Y. Hu, Z. Chen, X. Peng, L. Liu et al., A carbon aerogel with super mechanical and sensing performances for wearable piezoresistive sensors. J. Mater. Chem. A 7, 8092–8100 (2019). https://doi.org/10.1039/c9ta00596j
Z. Wei, D. Wang, S. Kim, S.-Y. Kim, Y. Hu et al., Nanoscale tunable reduction of graphene oxide for graphene electronics. Science 328, 1373–1376 (2010). https://doi.org/10.1126/science.1188119
I. You, D.G. Mackanic, N. Matsuhisa, J. Kang, J. Kwon et al., Artificial multimodal receptors based on ion relaxation dynamics. Science 370, 961–965 (2020). https://doi.org/10.1126/science.aba5132
J. Park, M. Kim, Y. Lee, H.S. Lee, H. Ko, Fingertip skin–inspired microstructured ferroelectric skins discriminate static/dynamic pressure and temperature stimuli. Sci. Adv. 1, e1500661 (2015). https://doi.org/10.1126/sciadv.1500661
C. Chen, Y. Kuang, S. Zhu, I. Burgert, T. Keplinger et al., Structure–property–function relationships of natural and engineered wood. Nat. Rev. Mater. 5, 642–666 (2020). https://doi.org/10.1038/s41578-020-0195-z
H. Zou, Y. Zhang, L. Guo, P. Wang, X. He et al., Quantifying the triboelectric series. Nat. Commun. 10, 1427 (2019). https://doi.org/10.1038/s41467-019-09461-x
Z.L. Wang, Triboelectric nanogenerators as new energy technology for self-powered systems and as active mechanical and chemical sensors. ACS Nano 7, 9533–9557 (2013). https://doi.org/10.1021/nn404614z
Z.L. Wang, On Maxwell’s displacement current for energy and sensors: the origin of nanogenerators. Mater. Today 20, 74–82 (2017). https://doi.org/10.1016/j.mattod.2016.12.001