一个基于机器学习的概率计算框架,用于确定不确定性量化聚类延伸度结构的执行
Yipeng Ge1, Zigang He1, Shaofan Li2
1College of Aerospace Engineering, Chongqing University, Chongqing, 400044 People's Republic of China.
概括
这项研究引入了一种数据驱动的方法,用于在集群的延伸度结构中量化不确定性,从而能够精确控制软机器人变形. 该方法使用机器学习来预测响应并优化灵活操纵器的执行.
科学领域:
- 机器人与机械工程 机器人与机械工程
- 计算科学 计算科学
- 数据科学数据科学数据科学
背景情况:
- 集群式十分位结构为灵活的操纵器和软机器人提供轻量级,可折叠和可部署的解决方案.
- 这些软结构的执行具有很高的概率灵敏度,需要精确的不确定性量化和变形控制.
研究的目的:
- 开发一个全面的数据驱动的计算框架,用于不确定性量化 (UQ) 和概率传播在集群的密度结构.
- 创建一个替代优化模型来精确控制柔性结构变形.
主要方法:
- 利用机器学习方法,包括高斯过程回归 (GPR) 和神经网络 (NN),以克服非线性有限元分析 (FEA) 趋同挑战.
- 开发了一个替代模型,用于实时预测不确定性传播.
- 实现了序列二次编程 (SQP) 和贝叶斯优化,用于控制执行变形.
主要成果:
- 证明了数据驱动方法的有效性,使用受集群启动的集群张密度束.
- 实现了快速的实时预测,用于不确定性传播.
- 成功优化了柔性结构的执行变形.
结论:
- 拟议的数据驱动计算方法是有效的UQ和变形控制在集群的张正度结构.
- 该框架显示了扩展到其他UQ模型和软机器人和灵活操纵器的优化目标的潜力.
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