Data-driven geometric system identification for shape-underactuated dissipative systems
Data-driven geometric system identification for shape-underactuated dissipative systems
复制标题
形状欠驱动耗散系统的数据驱动几何系统识别
DOI：
10.1088/1748-3190/ac3b9c
复制
发表时间：
2021
影响因子：
3.4
通讯作者：
Revzen, Shai
中科院分区：
文献类型：
--
作者：
Bittner, Brian Arthur;Hatton, Ross L;Revzen, Shai
当单个系统组件的属性无法直接测量时，系统动力学建模变得具有挑战性，并且通常需要从观察到的运动中识别属性。在本文中，我们表明运动高度耗散的系统具有比一般技术更容易识别模型和更快速地优化运动的功能。几何力学提供了通过环境均匀性来减少动力学的方法，而耗散性质最大限度地减少了动力学中二阶（惯性）特征的作用。在这里，我们将几何系统识别工具扩展到“形状欠驱动耗散系统（SUDS）”——其运动比惯性更耗散的系统，但其驱动仅限于身体形状坐标的子集。许多动物的运动都是 SUDS，包括线虫和有鞭毛细菌等微型游泳者，以及蛇和蜥蜴等颗粒状运动者。许多软机器人也是 SUDS，特别是采用高阻尼串联弹性执行器以降低运动和操作过程中与环境相互作用的刚性的机器人。我们鼓励使用 SUDS 模型，并验证其预测各种模拟粘性游泳平台运动的能力。对于一大类 SUDS，我们展示了如何将形状速度致动输入直接转换为扭矩输入，这表明可以使用所提供的工具对具有软气动或介电弹性体致动器的系统进行建模。基于物理学的基本假设，我们展示了我们的模型复杂性如何与被动形状坐标的数量线性缩放。这种缩放大大减少了从实验数据中识别系统模型所需的试验数量，并且可以减少过度拟合。我们的方法的样本效率表明它可用于机器人技术的建模、控制和优化，以及作为研究摩擦主导状态下的有机体运动的工具。
Modeling system dynamics becomes challenging when the properties of individual system components cannot be directly measured, and often requires identification of properties from observed motion. In this paper, we show that systems whose movement is highly dissipative have features which provide an opportunity to more easily identify models and more quickly optimize motions than would be possible with general techniques. Geometric mechanics provides means for reduction of the dynamics by environmental homogeneity, while the dissipative nature minimizes the role of second order (inertial) features in the dynamics. Here we extend the tools of geometric system identification to'shape-underactuated dissipative systems (SUDS)'—systems whose motions are more dissipative than inertial, but whose actuation is restricted to a subset of the body shape coordinates. Many animal motions are SUDS, including micro-swimmers such as nematodes and flagellated bacteria, and granular locomotors such as snakes and lizards. Many soft robots are also SUDS, particularly robots that incorporate highly damped series elastic actuators to reduce the rigidity of their interactions with their environments during locomotion and manipulation. We motivate the use of SUDS models, and validate their ability to predict motion of a variety of simulated viscous swimming platforms. For a large class of SUDS, we show how the shape velocity actuation inputs can be directly converted into torque inputs, suggesting that systems with soft pneumatic or dielectric elastomer actuators can be modeled with the tools presented. Based on fundamental assumptions in the physics, we show how our model complexity scales linearly with the number of passive shape coordinates. This scaling offers a large reduction on the number of trials needed to identify the system model from experimental data, and may reduce overfitting. The sample efficiency of our method suggests its use in modeling, control, and optimization in robotics, and as a tool for the study of organismal motion in friction dominated regimes.
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