基于收缩执行器的软体机器人心室预测设计框架

中文摘要 AI 辅助

自然心动周期分为收缩期和舒张期，涵盖四个不同阶段：收缩期的等容收缩和射血，以及舒张期的等容舒张和充盈。心血管建模从高保真多物理场模拟到降阶集总参数（Windkessel）表示的心-动脉耦合。然而，当前模型未将驱动心室泵的执行器力学与血流动力学联系起来。在本研究中，我们开发并实验验证了一个使用多种软体收缩执行器的心室样泵预测设计框架。我们构建了一个循环回路，可复现整个循环，包括等容阶段——其中压力变化而无体积变化。该框架基于集总参数模型，以下称为相位依赖的执行器驱动三元件Windkessel（AWK3）模型，以桥接软体执行器力学与心脏压力-容积（P-V）环。与传统模型需固定压力或容积作为输入以估计另一个不同，我们提出的模型在执行器等长特性信息下可预测两个变量。我们使用由线性收缩串联弹性执行器或扭曲和卷曲聚合物执行器（TCPA）驱动的心室启发泵验证模型。我们将执行器等长测试协议与相位依赖的AWK3模型关联，该模型复制了Frank-Starling定律，准确描述了前负荷、后负荷和肌力（收缩性）变化条件下的心脏行为。该方法为仿生软体机器人循环系统的设计和高保真控制提供了稳健平台。

英文摘要

The natural cardiac cycle is divided into the systole and diastole phases which encompass four distinct stages: isovolumetric contraction and ejection during systole, followed by isovolumetric relaxation and filling during diastole. Cardiovascular modeling of this cycle ranges from high-fidelity multiphysics simulations to reduced-order lumped-parameter (Windkessel) representation of the heart-artery coupling. However, current models do not relate the mechanics of the actuator driving the ventricle pump to the hemodynamics. In this study, we develop and experimentally validate a predictive design framework for ventricle-like pumps using various types of soft contractile actuators. We build a circulatory loop which reproduces the entire loop including the isovolumetric phases-where pressure changes occur without volume shifts. The framework is based on a lumped-parameter model, hereafter referred to as the phase-dependent Actuator-driven Windkessel 3-element (AWK3) model, to bridge soft actuator mechanics to the cardiac pressure-volume (P-V) loop. Unlike traditional models that require either pressure or volume as a fixed input to estimate the other, our proposed model predicts both variables when informed by the isometric characteristics of the actuators. We validate the model using a ventricle-inspired pump driven by a linear contractile series-elastic actuator or twisted and coiled polymer actuators (TCPA). We relate the actuator isometric testing protocol to the phase-dependent AWK3 model, which replicates the Frank-Starling law, accurately describing cardiac behavior under varying conditions of preload, afterload, and inotropy (contractility). This approach provides a robust platform for the design and high-fidelity control of bio-inspired soft robotic circulatory systems.

发表机构

• University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
• Loma Linda University Health（洛马琳达大学健康中心）
• Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校贝克曼先进科学技术研究所）

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