调查跟踪性能和能量之间的权衡在异质变量招募流动的人工肌肉捆绑中
Nicholas Mazzoleni1, Matthew Bryant1
1Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC 27606, United States of America.
Bioinspiration & biomimetics
|July 17, 2024
概括
可变招募流体人工肌肉 (FAM) 捆绑通过模仿生物肌肉招募来提高能源效率. 本研究探讨了FAM捆绑的最佳招聘订单和策略,发现Henneman的大小原则是有效的,但并不总是最佳的.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 生物启发工程 生物启发工程
- 人工肌肉的人工肌肉
背景情况:
- 传统的液压执行器由于对峰值负载的固定尺寸而遭受能量损失.
- 可变招募流体人工肌肉 (FAM) 捆绑提供了通过选择性激活肌肉单元 (MU) 来提供生物灵感的解决方案.
- 现有的研究主要研究同质的FAM捆绑,与通过Henneman的大小原理运行的异质哺乳动物肌肉不同.
研究的目的:
- 调查可变招聘FAM捆绑的最佳招聘订单和战略.
- 分析不同MU大小分布和招聘订单的跟踪性能和能量之间的权衡.
- 为了确定招聘状态过渡值对FAM捆绑业绩的影响.
主要方法:
- 两项案例研究与不同的FAM捆绑MU大小分布和总力量容量进行了研究.
- 帕雷托边界是为了分析各种MU大小分布,招聘订单和过渡值之间的权衡而构建的.
- 基于跟踪性能和能量,评估了FAM捆绑的性能.
主要成果:
- 捆绑大小,MU分布,招聘顺序和过渡值之间存在复杂的关系,影响最佳绩效权衡.
- 亨纳曼的大小原则被证实是可行的招聘策略,虽然不是普遍优越的.
- 该研究强调需要动态招聘计划,根据系统条件调整值和订单.
结论:
- 可变招募的FAM捆绑器在传统执行器上提供了显著的能量优势.
- 优化FAM捆绑性能需要考虑异质的MU分布和可适应的招聘策略,超出亨纳曼的大小原则.
- 未来的工作应该集中在共同设计捆绑大小,MU分布和增强机器人系统的动态招聘控制上.
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