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arXiv 2307.01062cs.ROcs.SYeess.SY
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面向低带宽执行器动力学系统几何建模的数据驱动方法

A Data-Driven Approach to Geometric Modeling of Systems with Low-Bandwidth Actuator Dynamics

Johns Hopkins University(约翰斯·霍普金斯大学);Johns Hopkins University Applied Physics Lab(约翰斯·霍普金斯大学应用物理实验室)

Siming Deng, Junning Liu, Bibekananda Datta, Aishwarya Pantula, David H. Gracias, Thao D. Nguyen, Brian A. Bittner, Noah J. Cowan

AI总结:

针对软体机器人欠驱动高维动力学辨识难题,提出基于几何力学的数据驱动连接模型框架,并在游泳机器人与水凝胶爬行器仿真中验证及优化控制波形。

摘要:

由于软体机器人具有欠驱动、高维动力学特性,对其进行系统辨识颇具挑战。本文提出一种基于几何力学(也称规范理论)的数据驱动建模框架,可应用于对系统内部构型实施低带宽控制的系统。该方法利用来自随机扰动、重复行为的数据点,构建一系列由执行器动力学与运动动力学组成的连接模型。通过从具有对称性的耗散拉格朗日系统的一般公式推导这些连接模型,我们提供了一种可广泛应用于具有一阶低通执行器动力学的机器人的方法,包括水凝胶爬行器中使用的溶胀驱动执行器。这些模型准确捕捉了简化游泳机器人模型的系统形状和身体运动动力学。我们进一步将该方法应用于一个刺激响应型水凝胶仿真器,该仿真器刻画了驱动生物医学相关微型机器形状变化的化学—力学相互作用的复杂性。最后,我们提出一种通过迭代改进模型来数值优化控制信号的方法,并将其用于优化水凝胶爬行器的输入波形。这种向真实环境的迁移为运动设计和生物医学工程中的应用带来了前景。

英文摘要:

It is challenging to perform system identification on soft robots due to their underactuated, high-dimensional dynamics. In this work, we present a data-driven modeling framework, based on geometric mechanics (also known as gauge theory) that can be applied to systems with low-bandwidth control of the system's internal configuration. This method constructs a series of connected models comprising actuator and locomotor dynamics based on data points from stochastically perturbed, repeated behaviors. By deriving these connected models from general formulations of dissipative Lagrangian systems with symmetry, we offer a method that can be applied broadly to robots with first-order, low-pass actuator dynamics, including swelling-driven actuators used in hydrogel crawlers. These models accurately capture the dynamics of the system shape and body movements of a simplified swimming robot model. We further apply our approach to a stimulus-responsive hydrogel simulator that captures the complexity of chemo-mechanical interactions that drive shape changes in biomedically relevant micromachines. Finally, we propose an approach of numerically optimizing control signals by iteratively refining models, which is applied to optimize the input waveform for the hydrogel crawler. This transfer to realistic environments provides promise for applications in locomotor design and biomedical engineering.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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