Computational Biomimetics of Winged Seeds

Authors:
Qiqin Le, Jiamu Bu, Yanke Qu, Bo Zhu, Tao Du

Abstract:
We develop a computational pipeline to facilitate the biomimetic design of winged seeds. Our approach leverages 3D scans of natural winged seeds to construct a bio-inspired design space by interpolating them with geodesic coordinates in the 3D diffeomorphism group. We formulate aerodynamic design tasks with probabilistic performance objectives and adapt a gradient- free optimizer to explore the design space and minimize the expectation of performance objectives efficiently and effectively. Our pipeline discovers novel winged seed designs that outperform natural counterparts in aerodynamic tasks, including long-distance dispersal and guided flight. We validate the physical fidelity of our pipeline by showcasing paper models of selected winged seeds in the design space and reporting their similar aerodynamic behaviors in simulation and reality.

Video:

Paper:
Download paper here

Code:
Github Project Page

Acknowledgements:
Tsinghua authors thank Yuchen Sun, Jingyuan Hu, Professor Li Yi, Professor Weixin Huang, and Professor Xitong Liang for their suggestions on implementation, application, and fabrication of winged seed models. Tao Du acknowledges support from Tsinghua University and Shanghai Qi Zhi Institute. Bo Zhu thanks Side Effects Software Inc. (SESI) for providing a Houdini educational license.

Citation:

@article{10.1145/3687899,
author = {Le, Qiqin and Bu, Jiamu and Qu, Yanke and Zhu, Bo and Du, Tao},
title = {Computational Biomimetics of Winged Seeds},
year = {2024},
issue_date = {December 2024},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {43},
number = {6},
issn = {0730-0301},
url = {https://doi.org/10.1145/3687899},
doi = {10.1145/3687899},
abstract = {We develop a computational pipeline to facilitate the biomimetic design of winged seeds. Our approach leverages 3D scans of natural winged seeds to construct a bio-inspired design space by interpolating them with geodesic coordinates in the 3D diffeomorphism group. We formulate aerodynamic design tasks with probabilistic performance objectives and adapt a gradient-free optimizer to explore the design space and minimize the expectation of performance objectives efficiently and effectively. Our pipeline discovers novel winged seed designs that outperform natural counterparts in aerodynamic tasks, including long-distance dispersal and guided flight. We validate the physical fidelity of our pipeline by showcasing paper models of selected winged seeds in the design space and reporting their similar aerodynamic behaviors in simulation and reality.},
journal = {ACM Trans. Graph.},
month = nov,
articleno = {180},
numpages = {13},
keywords = {computational design, winged seeds}
}

Seed Samples:

Pipeline:

Experiments (Simulation):

Experiments (Fabrication):