基于机器学习的被动地址机器人变形表面 (PARMS)
Jue Wang1, Michael Sotzing1, Mina Lee1
1Department of Mechanical Engineering, Purdue University, 500 Central Dr, Lafayette, IN 47907, USA.
Science advances
|July 21, 2023
概括
研究人员使用离子执行器和被动矩阵定位开发了一个机器人变形表面. 这项创新简化了复杂表面的控制,使人机界面和机器人技术的应用成为可能.
科学领域:
- 机器人和人机界面 机器人和人机界面
- 材料科学 材料科学 材料科学
- 控制系统工程 控制系统工程
背景情况:
- 可重新配置的变形表面为先进的人机界面和生物灵感机器人技术提供了潜力.
- 开发高效的控制接口和算法对于变形表面的广泛采用至关重要.
- 现有的系统往往需要复杂的控制接口,用于大量的执行器.
研究的目的:
- 引入一个被动地址的机器人变形表面 (PARMS),控制复杂性降低.
- 开发一个实时,高精度的控制算法,用于动态表面变形.
- 通过简化控制接口,根据需求实现任意的表面配置.
主要方法:
- 用于变形表面的矩阵排列的离子执行器.
- 实现了被动矩阵定位,将控制输入从N^2减少到2N.
- 采用机器学习培训,使用有限元模拟来开发控制算法.
- 实现了前向和反向控制,用于动态表面变形.
主要成果:
- 展示了一个被动地址的机器人变形表面 (PARMS),控制界面的复杂性大大降低.
- 启用实时,高精度控制动态表面变形.
- 成功地将PARMS转化为随意可实现的预定义表面.
- 验证了被动矩阵定位和机器学习控制的有效性.
结论:
- 开发的PARMS及其控制算法为更简单,更高效的可重新配置表面提供了一条途径.
- 这项技术有可能在可穿戴设备,触觉和增强现实/虚拟现实中实现先进应用.
- 被动矩阵定位是减少控制大型执行器阵列复杂性的关键创新.
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