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Deformable linear objects (DLOs), such as rods, cables, and ropes, play important roles in daily life. However, manipulation of DLOs is challenging as large geometrically nonlinear deformations may occur during the manipulation process. This problem is made even more difficult as the different deformation modes (e.g., stretching, bending, and twisting) may result in elastic instabilities during manipulation. In this paper, we formulate a physics-guided data-driven method to solve a challenging manipulation task—accurately deploying a DLO (an elastic rod) onto a rigid substrate along various prescribed patterns. Our framework combines machine learning, scaling analysis, and physical simulations to develop a physics-based neural controller for deployment. We explore the complex interplay between the gravitational and elastic energies of the manipulated DLO and obtain a control method for DLO deployment that is robust against friction and material properties. Out of the numerous geometrical and material properties of the rod and substrate, we show that only three non-dimensional parameters are needed to describe the deployment process with physical analysis. Therefore, the essence of the controlling law for the manipulation task can be constructed with a low-dimensional model, drastically increasing the computation speed. The effectiveness of our optimal control scheme is shown through a comprehensive robotic case study comparing against a heuristic control method for deploying rods for a wide variety of patterns. In addition to this, we also showcase the practicality of our control scheme by having a robot accomplish challenging high-level tasks such as mimicking human handwriting, cable placement, and tying knots. 

A new class of thin flexible structures is introduced that morph from flat into prescribed 3D shapes through strain mismatch between layers of a composite plate. To achieve control over the target shape, two different concepts are coupled. First, motivated by biological growth, strain mismatch is applied between the flat composite layers to transform it into a 3D shape. Depending on the amount of the applied strain mismatch, the transformation involves buckling into one of the available finite number of deformation modes. Second, inspired by kirigami, portions of the material are removed from one of the layers according to a specific pattern. This dramatically increases the number of possible 3D shapes and allows us to attain specific topologies. An experimental apparatus that allows precise control of the strain mismatch is devised. An inverse problem is posed, where starting from a given target shape, the physical parameters that make these shapes possible are determined. To show how the concept works, it focuses on circular composite plates and designs a kirigami pattern that yields a hemispherical structure. The analysis combines a theoretical approach with numerical simulations and physical experiments to understand and predict the shape transition from 2D to 3D. The tools developed here can be extended to attain arbitrary 3D shapes. The initially flat shape suggests that conventional additive manufacturing techniques can be used to functionalize the soft kirigami composite to fabricate, for example, deployable 3D structures, smart skins, and soft electromagnetic metasurfaces. 

Abstract Changing the surface properties (i.e., roughness or friction) can be instrumental for many applications but can be a complex and resource-intensive process. In this paper, we demonstrate a novel process of controlling the friction of a continuous rod by delivering inorganic microparticles. A standardized continuous particle transfer protocol has been developed in our laboratory for depositing particles from a liquid carrier system (LCS) to the cylindrical rod substrate. The particle transfer process can produce controllable and tunable surface properties. Polymeric binder is used to deliver the particles as asperities over the rod substrate and by controlling their size, shape, and distribution, the coefficient of friction of the rod is determined. Tabletop experiments are designed and performed to measure the friction coefficient following the Capstan equation. The entrained particles on the substrate will create size- and shape-based asperities, which will alter the surface morphology toward the desired direction. Both oblique and direct quantitative measurements are performed at different particles and binder concentrations. A systematic variation in the friction coefficient is observed and reported in the result section. It is observed from the capstan experiment that adding only 1% irregular shaped particles in the suspension changes the friction coefficient of the rods by almost 115%. The proposed friction control technique is a simple scale-up, low-cost, low-waste, and low-energy manufacturing method for controlling the surface morphology.