Metamaterials for reconfiguration and object classification

Soft metastructures have introduced new opportunities in shape adaptability and complexity reduction of soft robot sensing and controls. Metamaterials have recently been used to process information due to their ability to change its stiffness, adapt to different shapes, and complement traditional computing by introducing interaction with their environment. Metamaterials composed of different dome unit cells have gained interest due to their capabilities of exhibiting different energy minima, inversion path dependency, multiple global stable shapes, adaptability, and tunable stiffness. As each unit can be locally inverted, global scale shapes are generated due to the proximity between units and their interactions. As a result, the metastructure exhibits different global stable states depending on the unit shape and spatial distribution. This nonlinear behavior can be utilized to classify different external stimuli, informed only by the mechanical response of the structure and its interaction with the environment.

We report on metamaterial architectures exhibiting hybrid units that sense and classify spatial features distributed mechanical stimuli. Our work leverages the nonlinear bistable behavior of each unit cell, the mechanical interaction between units, and the strain amplification due to a dome inversion to process and extract discriminant information. We tailored the geometrical characteristic of the unit and unit distribution to increase the number of unique global stable states, increasing the classification and computational capabilities of the structure. The resulting metamaterial can capture unique features for different external stimuli and classify them into different families that present the same characteristics. This sheds further light on utilizing the mechanical response of systems and means to simplify data analysis, sensing, and control sequences.