Session: SYMP S-1: Integrated Systems

Paper Number: 141250

141250 - Embodied Tactile Sensing and Object Classification From Mechanics 

Programmable soft metastructures have introduced new opportunities to leverage geometrical nonlinearities that enable adaptability, [1] shape morphing [2], and mechanical computing [3], [4]. Recently, metamaterials have been used to process information by exploiting their ability to change shape and stiffness, conform to different bodies, and complement traditional mechanical computing by introducing interaction between material unit cells. Metamaterials composed of other dome-shaped units have gained interest due to their capabilities of exhibiting different energy minima [5], [6], inversion path dependency, multiple global stable shapes, adaptability, and tunable stiffness. As the unit cell architecture can be reversibly inverted at the local scale, programmable multistable shapes at the global scale are generated due to the local prestress and interaction between each unit. As a result, this metamaterial class can exhibit different global stable states depending on the unit shape, pattern array, and distance between units. These nonlinear deflections can be utilized to distinguish between different external stimuli, isolating their unique characteristics informed only by the mechanical response of the structure.

This study investigates the computational capabilities of multistable metastructures composed of different hybrid pattern dome units that enable strain sensing. We leverage the nonlinear behavior of each unit cell, the mechanical interaction between units, and the strain amplification due to a dome inversion to process information by classifying different classes of objects using unsupervised learning methods after they interact with our hybrid metamaterial. We examine different geometrical characteristics of the unit and unit distribution to extract unique object features, utilizing the sense data and tensor decomposition. We finalize our study by incorporating our metastructure into a soft robotic architecture that classifies and grasps objects in distinctive ways. This sheds further light on utilizing the mechanical response of systems and means to simplify data analysis, sensing, and control sequences.

References
[1]    J. P. Udani and A. F. Arrieta, “Programmable mechanical metastructures from locally bistable domes,” Extrem. Mech. Lett., vol. 42, p. 101081, 2021, doi: 10.1016/j.eml.2020.101081.
[2]    D. Melancon, A. E. Forte, L. M. Kamp, B. Gorissen, and K. Bertoldi, “Inflatable Origami: Multimodal Deformation via Multistability,” Adv. Funct. Mater., vol. 32, no. 35, p. 2201891, Aug. 2022, doi: 10.1002/adfm.202201891.
[3]    H. Yasuda et al., “Mechanical computing,” Nature, vol. 598, no. 7879, pp. 39–48, 2021, doi: 10.1038/s41586-021-03623-y.
[4]    K. S. Riley et al., “Neuromorphic Metamaterials for Mechanosensing and Perceptual Associative Learning,” Adv. Intell. Syst., p. 2200158, Oct. 2022, doi: 10.1002/aisy.202200158.
[5]    J. A. Faber, J. P. Udani, K. S. Riley, A. R. Studart, and A. F. Arrieta, “Dome-Patterned Metamaterial Sheets,” Adv. Sci., vol. 7, no. 22, p. 2001955, Nov. 2020, doi: 10.1002/advs.202001955.
[6]    J. P. Udani and A. F. Arrieta, “Taming geometric frustration by leveraging structural elasticity,” Mater. Des., vol. 221, p. 110809, Sep. 2022, doi: 10.1016/j.matdes.2022.110809.
 

Presenting Author: Andres Arrieta Purdue University

Presenting Author Biography: Dr. Andres F. Arrieta is an Associate Professor of Mechanical Engineering and Aeronautics and Astronautics Engineering (by courtesy) at Purdue University, where he leads the Programmable Structures Lab. Previously, he worked as a Group Leader at ETH Zurich’s CMAS Lab and as a Research Associate at the Dynamics and Oscillations Group at TU Darmstadt. He received his Ph.D. in Mechanical Engineering from the University of Bristol and his BEng from the Los Andes University, Bogota, Colombia.
Dr. Arrieta’s research focuses on investigating instabilities and nonlinearity in structural mechanics and the fundamental interaction between geometry, hierarchy, and nonlinearity to design structural systems with intrinsic properties enabling adaptation, autonomy, and environmental responsiveness. Current efforts concentrate on the modeling and designing of programmable structures, soft robotics, bioinspired design, embodied intelligence in structures, nonlinear metamaterials, and morphing structures. The Programmable Structures Lab’s work has been highlighted by several media outlets, including National Geographic and Nature’s News and Views.
He has received several personal awards, including the 2021 inaugural Emerging Leaders Award in Smart Materials and Structures (IOP Publications); NSF CAREER Award (2020); the ASME Gary Anderson Award (2018) for “outstanding contributions to the field of Adaptive Structures;” and the ETH Postdoctoral Fellowship (2012).

Authors:

Andres Arrieta
Juan Osorio
Vitor Farias Costa De Carvalho
Ana Estrada Gomez