形状欠驱动耗散系统的数据驱动几何系统辨识
Data-Driven Geometric System Identification for Shape-Underactuated Dissipative Systems
摘要：
运动具有高度耗散性的系统提供了一个机会，既可以轻松识别模型，又可以快速优化运动。几何力学提供了通过环境均匀性来减少动力学的手段，而耗散性质最小化了动力学中二阶（惯性）特征的作用。在这里，我们将几何系统辨识的工具扩展到“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一个子集。许多动物的运动都是SUDS，包括线虫和鞭毛细菌等微型游泳者，以及蛇和蜥蜴等颗粒运动。许多软体机器人也是SUDS，特别是那些使用高阻尼串联弹性致动器的机器人。无论是参与运动还是操纵，这些机器人通常用于与环境进行不那么严格的交互。我们鼓励使用的SUDS模型，并验证他们的能力来预测各种模拟粘性游泳平台的运动。对于一个大类的SUDS，我们展示了如何形状速度驱动输入可以直接转换为扭矩输入，这表明软气动执行器或介电弹性体系统可以建模与工具。基于物理学中的基本假设，我们展示了我们的模型复杂性如何随被动形状坐标的数量线性缩放。这大大减少了从实验数据中识别系统模型所需的试验次数，并且可以减少过拟合。我们的方法的样本效率表明其在机器人建模，控制和优化中的使用，并作为一种工具，在摩擦占主导地位的制度的有机体运动的研究。
英文摘要：
Systems whose movement is highly dissipative provide an opportunity to both identify models easily and quickly optimize motions. 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 those robots using highly damped series elastic actuators. Whether involved in locomotion or manipulation, these robots are often used to interface less rigidly with the environment. 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 actuators or dielectric elastomers 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 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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