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Shape‐morphing devices, a crucial branch in soft robotics, hold significant application value in areas like human–machine interfaces, biomimetic robotics, and tools for biological systems. To achieve 3D programmable shape morphing (PSM), the deployment of array‐based actuators is essential. However, a critical knowledge gap in 3D PSM is controlling the complex systems formed by these soft actuator arrays to mimic the morphology of the target shapes. This study, for the first time, represents the configuration of shape‐morphing devices using point cloud data and employing deep learning to map these configurations to control inputs. Shape Morphing Net (SMNet), a method that realizes the regression from point cloud to high‐dimensional control input vectors, is proposed. It has been applied to 3D PSM devices with three different actuator mechanisms, demonstrating its universal applicability to inversely reproduce the target shapes. Further, applied to previous 2D PSM devices, SMNet significantly enhances control precision from 82.23% to 97.68%. In the demonstrations of morphology mimicking, 3D PSM devices successfully replicate arbitrary target shapes obtained either through 3D scanning of physical objects or via 3D modeling software. The results show that within the deformable range of 3D PSM devices, accurate reproduction of the desired shapes is achievable.
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2D material programming for 3D shaping
Abstract Two-dimensional (2D) growth-induced 3D shaping enables shape-morphing materials for diverse applications. However, quantitative design of 2D growth for arbitrary 3D shapes remains challenging. Here we show a 2D material programming approach for 3D shaping, which prints hydrogel sheets encoded with spatially controlled in-plane growth (contraction) and transforms them to programmed 3D structures. We design 2D growth for target 3D shapes via conformal flattening. We introduce the concept of cone singularities to increase the accessible space of 3D shapes. For active shape selection, we encode shape-guiding modules in growth that direct shape morphing toward target shapes among isometric configurations. Our flexible 2D printing process enables the formation of multimaterial 3D structures. We demonstrate the ability to create 3D structures with a variety of morphologies, including automobiles, batoid fish, and real human face.
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- Award ID(s):
- 1848511
- PAR ID:
- 10211453
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 12
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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