All-printed soft human-machine interface for robotic physicochemical sensing
Abstract
Ultrasensitive multimodal physicochemical sensing for autonomous robotic decision-making has numerous applications in agriculture, security, environmental protection, and public health. Previously reported robotic sensing technologies have primarily focused on monitoring physical parameters such as pressure and temperature. Integrating chemical sensors for autonomous dry-phase analyte detection on a robotic platform is rather extremely challenging and substantially underdeveloped. Here, we introduce an artificial intelligence–powered multimodal robotic sensing system (M-Bot) with an all-printed mass-producible soft electronic skin–based human-machine interface. A scalable inkjet printing technology with custom-developed nanomaterial inks was used to manufacture flexible physicochemical sensor arrays for electrophysiology recording, tactile perception, and robotic sensing of a wide range of hazardous materials including nitroaromatic explosives, pesticides, nerve agents, and infectious pathogens such as SARS-CoV-2. The M-Bot decodes the surface electromyography signals collected from the human body through machine learning algorithms for remote robotic control and can perform in situ threat compound detection in extreme or contaminated environments with user-interactive tactile and threat alarm feedback. The printed electronic skin–based robotic sensing technology can be further generalized and applied to other remote sensing platforms. Such diversity was validated on an intelligent multimodal robotic boat platform that can efficiently track the source of trace amounts of hazardous compounds through autonomous and intelligent decision-making algorithms. This fully printed human-machine interactive multimodal sensing technology could play a crucial role in designing future intelligent robotic systems and can be easily reconfigured toward numerous practical wearable and robotic applications.
Additional Information
© 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Submitted 30 October 2021; Accepted 10 May 2022; Published 1 June 2022. We acknowledge critical support and infrastructure provided for this work by the Kavli Nanoscience Institute at Caltech. J.T. was supported by the National Science Scholarship from the Agency for Science, Technology and Research (A*STAR), Singapore. This work was supported by National Institutes of Health grant R01HL155815 (W.G.), Office of Naval Research grant N00014-21-1-2483 (W.G.), Translational Research Institute for Space Health grant NASA NNX16AO69A (W.G.), Tobacco-Related Disease Research Program grant R01RG3746 (W.G.), and Carver Mead New Adventures Fund at California Institute of Technology (W.G.). Author contributions: Conceptualization: W.G. and Y.Y. Methodology: Y.Y., J.L., and S.A.S. Investigation: Y.Y., J.L., S.A.S., J.M., J.T., W.G., C.X., and Y.S. Funding acquisition: W.G. Supervision: W.G. Writing—original draft: W.G., Y.Y., J.L., and S.A.S. Writing—review and editing: J.T., W.G., C.X., and Y.S. The authors declare that they have no competing interests. Data and materials availability: All data needed to support the conclusions of this manuscript are included in the main text or Supplementary Materials. The code for this study is available at https://github.com/Samwich1998/Robotic-Arm (sEMG-based robotic arm control) and https://github.com/Samwich1998/Boat-Search-Algorithm (M-Boat search algorithm).Attached Files
Accepted Version - nihms-1824323.pdf
Supplemental Material - scirobotics.abn0495_movies_s1_to_s6.zip
Supplemental Material - scirobotics.abn0495_sm.pdf
Files
Additional details
- PMCID
- PMC9302713
- Eprint ID
- 115016
- Resolver ID
- CaltechAUTHORS:20220603-368910100
- Agency for Science, Technology and Research (A*STAR)
- NIH
- R01HL155815
- Office of Naval Research (ONR)
- N00014-21-1-2483
- NASA
- NNX16AO69A
- California Tobacco-Related Disease Research Program
- R01RG3746
- Carver Mead New Adventures Fund
- Created
-
2022-06-03Created from EPrint's datestamp field
- Updated
-
2023-06-21Created from EPrint's last_modified field
- Caltech groups
- Kavli Nanoscience Institute