2025-09-30 カリフォルニア大学バークレー校（UCB）
Researchers designed a morphing helmet that could change its shape to shift protection to different regions of the head. (Image by Jianzhe Gu, Morphing Matter Lab)
＜関連情報＞
- https://engineering.berkeley.edu/news/2025/09/mighty-morphing-robots/
- https://www.nature.com/articles/s41467-025-63373-7
遺伝的アルゴリズムを用いた可変形状トラスシステムのアクチュエータネットワークの最適化と制御 Optimization and control of actuator networks in variable geometry truss systems using genetic algorithms
Jianzhe Gu,Ziwen Ye,Tucker Rae-Grant,Shuhong Wang,Ding Zhao,Josiah Hester,Victoria A. Webster-Wood & Lining Yao
Nature Communications Published:30 September 2025
DOI:https://doi.org/10.1038/s41467-025-63373-7
Abstract
A robot’s morphology is pivotal to its functionality, as biological organisms demonstrate through shape adjustments – octopi squeeze through small apertures, and caterpillars use peristaltic transformations to navigate complex environments. While existing robotic systems struggle to achieve precise volumetric transformations, Variable Geometry Trusses offer rich morphing capabilities by coordinating hundreds of actuating beams. However, control complexity scales exponentially with beam count, limiting implementations to trusses with only a handful of beams or to designs where only a subset of beams are actuable. Previous work introduced the metatruss, a truss robot that simplifies control by grouping actuators into interconnected pneumatic control networks, but relies on manual network design and control sequences. Here, we introduce a multi-objective optimization framework based on a tailored genetic algorithm to automate actuator grouping, contraction ratios, and actuation timing. We develop a highly damped dynamic simulator that balances computational efficiency with physical accuracy and validate our approach with experimental prototypes. Across multiple tasks, we demonstrate that the metatruss achieves complex shape adaptations with minimal control units. Our results reveal an optimal number of control networks, beyond which additional networks yield diminishing performance gains.
