SOFT ROBOTS: Control of soft robots with inertial dynamics
参考中译:软机器人：具有惯性动力学的软机器人控制


          

刊名：Science Robotics
作者：David A. Haggerty（Department of Mechanical Engineering, University of California）
Michael J. Banks（Department of Mechanical Engineering, University of California）
Ervin Kamenar（Department of Mechanical Engineering, University of California）
Alan B. Cao（Department of Electrical and Computer Engineering, University of California）
Patrick C. Curtis（Department of Mechanical Engineering, University of California）
Igor Mezic（Department of Mechanical Engineering, University of California）
Elliot W. Hawkes（Department of Mechanical Engineering, University of California）
刊号：737B0239/I
出版年：2023
年卷期：2023, vol.8, no.81
页码：6864-1--6864-14
总页数：14
分类号：TP24
语种：eng
文摘：Soft robots promise improved safety and capability over rigid robots when deployed near humans or in complex, delicate, and dynamic environments. However, infinite degrees of freedom and the potential for highly nonlinear dynamics severely complicate their modeling and control. Analytical and machine learning methodologies have been applied to model soft robots but with constraints: quasi-static motions, quasi-linear deflections, or both. Here, we advance the modeling and control of soft robots into the inertial, nonlinear regime. We controlled motions of a soft, continuum arm with velocities 10 times larger and accelerations 40 times larger than those of previous work and did so for high-deflection shapes with more than 110° of curvature. We leveraged a data-driven learning approach for modeling, based on Koopman operator theory, and we introduce the concept of the static Koopman operator as a pregain term in optimal control. Our approach is rapid, requiring less than 5 min of training; is computationally low cost, requiring as little as 0.5 s to build the model; and is design agnostic, learning and accurately controlling two morphologically different soft robots. This work advances rapid modeling and control for soft robots from the realm of quasi-static to inertial, laying the ground-work for the next generation of compliant and highly dynamic robots.
参考中译：当软机器人部署在人类附近或复杂、精细和动态的环境中时，与刚性机器人相比，软机器人有望提高安全性和能力。然而，无限多的自由度和高度非线性动力学的可能性严重地使它们的建模和控制变得复杂。分析和机器学习方法已被应用于软机器人的建模，但带有约束：准静态运动、准线性偏转或两者兼而有之。在这里，我们将软机器人的建模和控制推进到惯性、非线性区域。我们控制了一个柔软、连续的手臂的运动，速度是以前工作的10倍，加速度是以前工作的40倍，对于曲率超过110°的大挠度形状也是如此。我们利用基于Koopman算子理论的数据驱动学习方法进行建模，并引入静态Koopman算子的概念作为最优控制中的预增益项。我们的方法快速，只需要不到5分钟的训练；计算成本低，只需要0.5%的S就可以建立模型；并且不依赖于设计，学习和精确控制两个形态不同的软机器人。这项工作将软机器人的快速建模和控制从准静态领域推进到惯性领域，为下一代柔顺和高动态机器人奠定了基础。



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