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软气动肌肉:通过几何设计和智能控制来彻底改变人类辅助设备
Mahmoud Elsamanty1,2, Mohamed A Hassaan1, Mostafa Orban1,3,4
1Mechanical Department, Faculty of Engineering at Shoubra, Benha University, Cairo 11672, Egypt.
Micromachines
|July 29, 2023
概括
这项研究引入了软气动肌肉的新几何参数,增强了用于预测其行为的分析模型. 人工神经网络准确地预测压力值,用于软机器人应用中的精确定位.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 软机器人利用灵活的材料来实现更安全的人际交互和微妙的对象操纵.
- 软气动执行器 (SPM) 是软机器人的一个关键组成部分.
- 预测SPM的行为对于其有效的设计和应用至关重要.
研究的目的:
- 介绍软的人工气动肌肉的新型几何参数.
- 开发分析模型来预测SPM执行行为.
- 使用人工神经网络 (ANN) 预测压力值以准确定位.
主要方法:
- 研究了间和调动条件对SPM变形和应力的影响.
- 使用有限元分析 (FEA) 进行超弹性材料建模.
- 训练了一名ANN,根据所需的笛卡尔位置来预测内部压力.
主要成果:
- 新的几何参数有助于预测SPM执行.
- FEA模拟提供了对材料变形和应力的洞察.
- 经过培训的ANN在预测压力值方面取得了很高的准确性 (残留量>99%).
- 对ANN预测的最大坐标误差低于9.3%.
结论:
- 该研究成功地引入了软的人工气动肌肉的新参数.
- 开发的分析模型和ANN为预测SPM行为提供了有效的工具.
- 通过准确的压力预测,ANN在实现所需位置方面表现出高效率.
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