软式气动段的无模型控制
Jorge Francisco García-Samartín1, Raúl Molina-Gómez1, Antonio Barrientos1
1Centro de Automática y Robótica (UPM-CSIC), Universidad Politécnica de Madrid-Consejo Superior de Investigaciones Científicas, José Gutiérrez Abascal 2, 28006 Madrid, Spain.
Biomimetics (Basel, Switzerland)
|March 27, 2024
概括
本研究提出了使用神经网络和新型传感器的软机器人手臂的精确闭链控制方法. 该系统实现了低位置误差,提升了软机器人控制能力.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 材料科学 材料科学 材料科学
背景情况:
- 软机器人由于复杂的执行器和传感器动态而难以精确控制.
- 现有的控制方法往往缺乏软机器人系统的稳定性和准确性.
- 气动软臂需要复杂的控制,以确保可靠的操作.
研究的目的:
- 为模块化气动软臂 (PAUL) 开发一个封闭链控制策略.
- 为了利用基于弹性体的电阻传感器,对PAUL具有一致的负压电阻行为.
- 为了提高软机器人手臂控制的精度和可靠性.
主要方法:
- 实现了PAUL软臂部分的封闭链控制.
- 采用了以弹性体为基础的电阻传感器,具有独立于温度的负压电阻.
- 利用两个神经网络来翻译位置引用和估计膀膨胀状态.
- 评估系统的模块化性和在外部负载下的性能.
主要成果:
- 实现了4.59mm的位置误差,超过了现有的软机器人控制方法.
- 证明了基于神经网络的控制方法的有效性.
- 在不同的条件下验证了负压电阻传感器的可靠性.
- 在外部负载下确认了强大的性能,突出了系统的模块化.
结论:
- 开发的控制方法显著提高了软机器人手臂的精度.
- 专用传感器和神经网络的使用为难以建模的软机器人提供了可行的解决方案.
- 保罗的模块化设计提高了其在复杂场景中的适应性和性能.
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