使用遗传算法,优化和控制可变几何结构结构系统中的执行器网络
Jianzhe Gu1,2, Ziwen Ye2, Tucker Rae-Grant2
1Morphing Matter Lab, Mechanical Engineering, University of California, Berkeley, CA, USA.
Nature communications
|October 1, 2025
概括
本研究介绍了一种自动化方法,用于使用遗传算法来改变机器人的形状,以优化可变几何结构托架中的气动控制网络. 这种方法可以实现复杂的变形功能,并简化控制以增强机器人功能.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 形态学计算 形态学计算
- 生物启发工程 生物启发工程
背景情况:
- 机器人形态对于功能至关重要,模仿生物形状适应.
- 可变几何托架提供变形能力,但面临控制复杂性的挑战.
- 现有的metatruss设计需要手动控制网络配置.
研究的目的:
- 为了自动化对大铁路的控制网络的设计.
- 为了优化执行器分组,收缩比和执行时间,用于复杂的形状适应.
- 为了减少变形机器人系统的控制复杂性.
主要方法:
- 使用定制的遗传算法开发了一个多目标优化框架.
- 创建了一个高度缓和的动态模拟器,平衡计算效率和物理精度.
- 在多个任务中通过实验原型验证了方法.
主要成果:
- 成功自动化执行器分组和控制序列的metatrusses.
- 证明了复杂的形状适应与显著减少的控制单元.
- 确定了控制网络的最佳数量,以提高性能.
结论:
- 自动化框架使变形机器人的高效和复杂的形状适应成为可能.
- 优化的元轴承为先进的机器人形态提供了可行的解决方案.
- 该研究强调了控制网络数量和性能优势之间的权衡.
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