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.

中文翻译：

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有翅种子的计算仿生学


我们开发了一个计算管道来促进有翅种子的仿生设计。我们的方法利用天然有翅种子的 3D 扫描，通过使用 3D 微分同构组中的测地线坐标进行插值来构建一个仿生设计空间。我们制定具有概率性能目标的空气动力学设计任务，并采用无梯度优化器来探索设计空间并高效且有效地最小化对性能目标的期望。我们的管道发现了新颖的有翼种子设计，这些设计在空气动力学任务（包括长距离传播和引导飞行）中优于自然同类。我们通过在设计空间中展示选定的有翼种子的纸质模型，并在仿真和现实中报告它们类似的空气动力学行为，来验证管道的物理保真度。