控制具有惯性动态的软机器人
David A Haggerty1, Michael J Banks1, Ervin Kamenar1,2
1Department of Mechanical Engineering, University of California, Santa Barbara, CA 93106, USA.
Science robotics
|August 30, 2023
概括
这项研究介绍了一种基于数据的快速模型和控制软机器人的方法,使得比以前更快,更动态的运动成为可能. 该方法使用库普曼运算子理论来有效控制复杂的非线性机器人系统.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制理论 控制理论
- 机器学习 机器学习
背景情况:
- 软机器人在以人为中心和复杂的环境中提供了增强的安全性和能力.
- 由于无限度的自由度和非线性动态,模拟和控制软机器人具有挑战性.
- 现有的方法通常将软机器人控制限制在准静态运动或准线性偏移上.
研究的目的:
- 推进软机器人的建模和控制,使其进入惯性和非线性动态状态.
- 为准确的软机器人建模和控制开发一种快速,数据驱动的方法.
- 在软机器人系统中实现高速和高偏移运动.
主要方法:
- 利用库普曼运算子理论进行数据驱动的建模方法.
- 介绍了静态库普曼运算符作为最佳控制中的预收益术语.
- 训练并控制了两种形态上不同的软机器人.
主要成果:
- 实现了软机器人运动的控制,速度是前一项工作的10倍,加速是前一项工作的40倍.
- 成功控制了超过110°曲率的高偏斜形状.
- 证明了模型快速训练 (<5分钟) 和模型构建的低计算成本 (0.5秒).
结论:
- 开发的方法允许在惯性,非线性状态下快速建模和控制软机器人.
- 这项工作克服了软机器人的准静态和准线性模型的局限性.
- 铺平了下一代合规,高度动态的软机器人的道路.
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