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Abstract Miniature shape‐morphing soft actuators driven by external stimuli and fluidic pressure hold great promise in morphing matter and small‐scale soft robotics. However, it remains challenging to achieve both rich shape morphing and shape locking in a fast and controlled way due to the limitations of actuation reversibility and fabrication. Here, fully 3D‐printed, sub‐millimeter thin‐plate‐like miniature soft hydraulic actuators with shape memory effect (SME) for programable fast shape morphing and shape locking, are reported. It combines commercial high‐resolution multi‐material 3D printing of stiff shape memory polymers (SMPs) and soft elastomers and direct printing of microfluidic channels and 2D/3D channel networks embedded in elastomers in a single print run. Leveraging spatial patterning of hybrid compositions and expansion heterogeneity of microfluidic channel networks for versatile hydraulically actuated shape morphing, including circular, wavy, helical, saddle, and warping shapes with various curvatures, are demonstrated. The morphed shapes can be temporarily locked and recover to their original planar forms repeatedly by activating SME of the SMPs. Utilizing the fast shape morphing and locking in the miniature actuators, their potential applications in non‐invasive manipulation of small‐scale objects and fragile living organisms, multimodal entanglement grasping, and energy‐saving manipulators, are demonstrated.
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Harnessing Deep Learning of Point Clouds for Morphology Mimicking of Universal 3D Shape‐Morphing Devices
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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- Award ID(s):
- 2301509
- PAR ID:
- 10618572
- Publisher / Repository:
- Wiley Advanced
- Date Published:
- Journal Name:
- Advanced Intelligent Systems
- ISSN:
- 2640-4567
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1002/aisy.202400550
