Reversible Mechanical Deformations of Soft Microchannel Networks for Sensing in Soft Robotic Systems

Authors

Abhiteja Konda, Argonne National Laboratory Lemont & University of Nebraska–Lincoln
Donghee Lee, University of Nebraska Medical Center & University of Nebraska–LincolnFollow
Taesun You, Houston District Texas Department of Transportation
Xiaoyan Wang, University of Nebraska Medical Center
Sangjin Ryu, University of Nebraska-LincolnFollow
Stephen Morin, University of Nebraska - LincolnFollow

Date of this Version

2019

Citation

Adv. Intell. Syst. 2019, https://doi.org/10.1002/aisy.201900027.

Comments

2019 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Abstract

Microfluidics has enabled numerous applications in, for example, analytical chemistry, medical diagnostics, microelectronics, and soft robotics. In most of these applications, the geometries of the microchannels are of fixed dimensions that (ideally) remain invariant during operation. In soft robotics, however, the geometries of the microchannels contained in soft actuation systems are inherently dynamic, and the specific dimensions are expected to change during operation, and, by extension, the fluid transport properties of the system are variable. If this characteristic is not properly considered, or if methods are not developed to control it, the progress of soft robotic devices with distributed fluid transport systems can be hampered. Herein, the deformation of soft micro- channel networks is investigated using a finite-element method and experimental observations, and the understandings are applied to imbibe sensing capabilities in soft robots that have integrated microfluidic networks with dynamic channel geometries of predictable dimensions. This approach enables the fabrication of soft fluid transport systems with deterministic deformation characteristics—a capability that is specifically applied to touch and actuation sensing in soft actuators. This work provides insight into the channel deformation processes expected in soft robotic systems with embedded networks of microchannels, enabling devices with reliable/predictable transport properties.