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Cited by 26Dielectric materials and actuatorsAdvanced Materials and MechanicsSoft Robotics and Applications

Passively addressed robotic morphing surface (PARMS) based on machine learning

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Wang, Jue, Sotzing, Michael, Lee, Mina, Chortos, Alex·2023·Science Advances
DOI 10.1126/sciadv.adg8019PMC PMC10361599
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Abstract

Reconfigurable morphing surfaces provide new opportunities for advanced human-machine interfaces and bio-inspired robotics. Morphing into arbitrary surfaces on demand requires a device with a sufficiently large number of actuators and an inverse control strategy. Developing compact, efficient control interfaces and algorithms is vital for broader adoption. In this work, we describe a passively addressed robotic morphing surface (PARMS) composed of matrix-arranged ionic actuators. To reduce the complexity of the physical control interface, we introduce passive matrix addressing. Matrix addressing allows the control of N 2 independent actuators using only 2 N control inputs, which is substantially lower than traditional direct addressing ( N 2 control inputs). Using machine learning with finite element simulations for training, our control algorithm enables real-time, high-precision forward and inverse control, allowing PARMS to dynamically morph into arbitrary achievable predefined surfaces on demand. These innovations may enable the future implementation of PARMS in wearables, haptics, and augmented reality/virtual reality.

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Cited by

Sorted by citations

Authors
Wang, J., Sotzing, M., Lee, M., & Chortos, A.
Affiliations
Purdue University West Lafayette (US)
Corresponding author
Chortos, Alex
ORCID
Venue
Science Advances
Publisher
American Association for the Advancement of Science (AAAS)
Published
2023-07-21
Volume
9
Issue
29
Pages
eadg8019-eadg8019
ISSN
2375-2548 (ISSN-L)
DOI
10.1126/sciadv.adg8019
PMC
PMC10361599
OpenAlex
W4385063883
Cited by
26
References
3
Citations by year
2026: 8 · 2025: 9 · 2024: 8 · 2023: 1
FWCI
2.05
Type
journal article
Language
en
Open access
Licence
cc-by-nc
Locations
  • Science Advances (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
Funders
Purdue University
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Dielectric materials and actuators
  • Physical Sciences › Engineering › Mechanical Engineering › Advanced Materials and Mechanics
  • Physical Sciences › Engineering › Biomedical Engineering › Soft Robotics and Applications
Keywords
passive matrix addressing, inverse control, forward control, reconfigurable surfaces, machine learning control, finite element analysis, bio-inspired robotics, human-machine interaction, wearable devices, haptics, augmented reality

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