In animals and humans, trained neural networks form associative learning patterns that allow rapid classification and decision making (e.g. object identification via tactile inputs). In contrast, engineered systems are unable to sense and classify information without significant instrumentation and software. But this might be possible if a system were constructed in a way that combines materials for distributed sensing, intrinsic computing (for classification), and memory (for learning). Research by Arrieta, et al demonstrated an important step toward this goal. They showed that thin, flexible dome-patterned metamaterials, comprised of planar arrays of bistable domes, reconfigure their global shapes in emergent forms when specific domes are inverted. Most intriguing, the global shape of the sheet is dictated by both the local pattern and sequence of dome inversions, suggesting that the intrinsic mechanics of the sheet performs a nonlinear “computation” of local events.&nbsp;&nbsp;We propose these complex mechanics provide a foundation for combining autonomous reconfiguration with distributing sensing and embedded memory—akin to a peripheral nervous system—for onboard classification in engineered structures. One example outcome of this vision is a soft robot end-effector that “computes and learns” what it is interacting with by allowing the object to deform the shape of the end-effector. To achieve this goal, the local deformation states of the domes must be transduced and remembered.We demonstrate that dome inversion events can be sensed and remembered by integrating resistive strain sensors connected electrically to memory storage elements. The former is achieved by printing conductive traces into the dome-patterned metasheets at positions near the individual domes (as detailed in Hybrid materials with autonomous information processing part I), while the latter consists of polymeric thin-films that function as memory resistors. When the two are connected in series and biased with voltage, a transient change in sensor resistance caused by deformation of the dome encodes a permanent change in the resistance of the memristor. Through fabrication and experiments, we study the design and integration of both the sensor and memory storage elements for effective operation. An amplification circuit is used to magnify changes in sensor resistance, which allows the memristor voltage to eclipse its switching threshold. Using 1 sensor:1 memristor pairs, we demonstrate spatially-distributed transduction and memory of dome inversions, and we demonstrate incremental changes in the state of the memristor by applying AC voltage, which allows for temporal encoding of dome inversion events. These capabilities support a path toward robotic materials that autonomously classify tactile information.

Hybrid Materials with Autonomous Information Processing Part II: Integration of Sensing and Memory

Paper Proceedings: https://asmedigitalcollection.asme.org/SMASIS/SMASIS2021/volume/85499

ASME 2021 Smart Materials, Adaptive Structures and Intelligent Systems (SMASIS2021)

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