数据驱动的扭弦驱动用于轻量级和合规的人类形态敏捷的手
Zhiyao Zheng1, Jingwei Zhan2,3, Zhaochun Li1
1College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Biomimetics (Basel, Switzerland)
|September 26, 2025
概括
这项研究引入了一种新的方法来控制机器人手,使用数据驱动的建模和仿生设计. 它提高了轻型机器人手的精度和性能,用于复杂的交互.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 机械工程 机械工程
- 人工智能的人工智能
背景情况:
- 传统的机器人手动操作系统很重,复杂,缺乏合规性.
- 扭弦执行器 (TSA) 提供轻量级,合规的替代品,但在变量负载下难以实现非线性控制.
研究的目的:
- 开发一种高精度的控制方法,用于变量负载下的TSA.
- 使用创新的机制设计和验证一种轻量级的人形灵巧的机器人手.
主要方法:
- 一个双隐藏层的后传神经网络 (BPNN) 用于数据驱动的TSA位移预测.
- 设计了一种仿生,低调的五指灵巧的手,带有肌弹传动.
- 一个实验平台集成了BPNN-动态建模用于性能验证.
主要成果:
- 准确预测TSA在负载范围为0.1-4.2公斤下的位移.
- 实现了一个超轻量级的灵巧的手,具有精确的曲角度预测.
- 证明了多功能手势复制和强大的抓取,最大指尖力为7.4N.
结论:
- 集成的数据驱动建模和仿生设计为变量负载应用程序推进了TSA控制.
- 这项工作为创建高性能,轻量级巧妙的机器人手提供了一个新的范式.
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