一个游泳机器人的经验数据驱动线性模型,使用复杂的延迟嵌入DMD技术.
Mostafa Sayahkarajy1, Hartmut Witte1
1Group of Biomechatronics, Fachgebiet Biomechatronik, Technische Universität Ilmenau, D-98693 Ilmenau, Germany.
Biomimetics (Basel, Switzerland)
|January 24, 2025
概括
这项研究引入了一种数据驱动的方法,用于模拟软机器人中状机动的复杂动态. 该技术成功地从运动数据中提取基本模式,简化了机器人控制.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 流体动力学 流体动力学
- 生物模拟技术是生物模拟的
背景情况:
- 角状运动是一种有效的水生运动策略,涉及全身液体与身体的相互作用.
- 了解这种运动的复杂物理是开发先进机器人系统的关键.
- 数据驱动的方法提供了一种潜在的途径,可以在没有直接水力动力学测量的情况下建模这些动态.
研究的目的:
- 为软型机器人鱼提出经验动力控制和数据驱动的建模.
- 开发一种新的算法,从实验数据中提取动态模型.
- 分析和描述软机器人运动的基本动力学.
主要方法:
- 设计了一个由气动人工肌肉驱动的六段软机器人.
- 动力学方程被用来生成模仿 anguilliform 动力学所需的执行模式.
- 使用QualiSys®追踪管理器收集了机器人运动的实验数据.
- 开发并应用了一种新的复杂变量延迟嵌入动态模式分解 (CDE DMD) 算法.
主要成果:
- 该CDE DMD算法成功地从实验数据中提取了线性和混乱模式.
- 分析显示,机器人的动态可以通过具有间歇混乱行为的线性化模型近似.
- 拟议的方法有效地从有限的传感器测量中确定了连贯模式.
结论:
- 数据驱动的建模,特别是使用CDE DMD算法,可以有效地捕捉软机器人运动的复杂动态.
- 机器人运动的特点是可预测的线性动力学和不可预测的混乱模式的组合.
- 这种方法为理解和控制生物灵感机器人系统提供了一个强大的工具.
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