Mechanical Intelligence From Structural Multistability

<p dir="ltr">Systems in nature exhibit complex behaviors and unique functionalities by tuning their global properties, shapeshifting into functional forms, sensing various inputs, and adapting to external stimuli. Recently, mechanical metamaterials have been developed to mimic and extend these capabilities, by embedding mechanical intelligence and tunability of mechanical properties in engineering systems. Multistable metamaterials introduce new opportunities by leveraging snap-through instabilities to enable adaptability, shape morphing, and mechanical computing. These materials can process information by changing shape and stiffness, conforming to different structures, and complementing traditional mechanical computing through interactions between their fundamental unit cells. Multistable systems, while offering distinct advantages, bring unique design challenges that demand non-traditional design philosophies. To address these challenges, it is crucial to develop a solid understanding and reliable models for the nonlinear mechanics of this metamaterials to understand their full capabilities when incorporated into larger systems. This work contributes to understanding and modeling the nonlinear mechanics of multistable structural systems, focusing on metastructures composed of dome-shaped units. These units can be reversibly inverted at a local scale, generating a global response due to local prestress and unit interaction. As a result, this class of metamaterials can exhibit different global stable states depending on the unit shape, pattern array, and spacing, making them ideal for applications in morphological computing soft robots and structures with embedded mechanical intelligence.</p><p dir="ltr">In the first part of this thesis, we investigate the advantages of incorporating metastructures into soft robotic architectures. Soft robots are valued for their ability to interact with their environment, adapt to external stimuli, and protect against disturbances. Their intrinsic safety, derived from their manufacturing materials, allows them to perform tasks challenging for rigid robots. However, the highly nonlinear material response of soft robots makes controlling their specific configurations difficult, often requiring sophisticated sensors and complex closed-loop control systems. Multistable structures offer a new strategy by programming input-specific stable and defined shapes, providing a control strategy without closed-loop control. We studied pneumatically actuated soft robots with multiple, accessible, and stable states, enabling shape reconfiguration by incorporating metastructures into the soft robot topology. By leveraging the mechanical response of multistable metastructures, we encode different control set points, which can be attained via a single pressure input and open-loop control. Furthermore, we combined the different mechanical responses from the multistable structure to program unique robot behaviors at each of the programmed set points, enabling embodied robotic tasks and robot operation at different configurations. Informed by the mechanics of hierarchically multistable metastructures, we design soft structures with coexisting stable states by combining gripper-like geometries with dome-patterned metasheets. We leverage the distinct path-dependent activation sequences to access desired coexisting states, resembling different actuation modes in soft manipulators, including grasping and twisting. Using the interaction of the dome-shaped units, we demonstrate how to describe this system as a temporal finite state machine that yields different output shapes depending on the recorded sequence. Our strategy offers a new route for controlling soft robots by exploiting the nonlinear mechanics of multistable structures to the designer’s advantage, thus opening an avenue for embodied finite-state technology in soft structures.</p><p><br></p><p dir="ltr">To analyze the information processing capabilities of our metastructure, we enhance the metamaterial by incorporating strain sensors into each of the units (hybrid dome unit), enabling strain field sensing. We leverage the nonlinear behavior of each unit cell, the strain amplification due to dome inversion, and the interaction between domes to process information by classification and storage of different external stimuli. First, we physically encode a Hopfield network into our physical substrate, which learns the history of spatially distributed input patterns. Moreover, we use the interaction between units to classify different classes of objects using the unique strain field produced after they interact with our hybrid metamaterial. The geometrical characteristics of the unit and unit distribution are tailored to increase the number of unique global stable states, enhancing the classification and computational capabilities of the structure. The resulting metamaterial can capture unique features for different external stimuli and classify them into families with similar characteristics.</p><p><br></p><p dir="ltr">Finally, we develop a semi-analytical modeling framework to analyze and capture the behavior of the multistable metastructure. Structural response modeling is complex and computationally expensive due to the high dependency on unit geometry, inversion sequence, number of units, and spatial distribution. Therefore, simpler yet robust models are needed to predict the final state of the structure, enabling faster analysis for inverse design. We present a lumped-element model to determine the final shape of the metastructure based on the number of units, spatial distribution, and geometric parameters. This model explores the interrelations between units and their spatial arrangement, allowing us to target specific positions and understand the role of geometric frustration within the structure. This facilitates optimal strategies to leverage the material’s unique properties for soft robotics and mechanical computing.</p><p><br></p><p dir="ltr">The work done as part of this thesis sheds light on the implementation of multistable structures into soft structures for developing underactuated systems with adaptive and intelligent responses. It will also shed light on utilizing systems’ mechanical responses to simplify data analysis, sensing, control, and computation.</p><p><br></p>