建模软气动执行器的力-压-高关系，实现单压力输入下的智能举重设计。
Active Learning Design: Modeling Force Output for Axisymmetric Soft Pneumatic Actuators
- 通过能量最小化求解建立力-压-高理论模型，结合主动学习高效采集数据。
- 基于22个Ecoflex 00-30膜片实测数据，模型预测精度优于理论与拟合方法。
- 可用于不同举重任务的膜片结构优化，适合机器人柔性执行器设计者。
由弹性材料制成的软气动执行器（SPA）可提供大应变和大作用力。已有研究深入探讨了局部应变受限的超弹性材料在充气时的形状重构行为，但对外部受力条件下运动轨迹的研究仍不足。本文针对同心应变限制型软气动执行器，建模其力-压-高关系，并展示该模型在物体举升响应设计中的应用。通过求解能量最小化方程预测不同载荷下的关系，并利用自动化测试平台采集n=22个Ecoflex 00-30膜片的丰富数据，构建主动学习流程以高效探索设计空间。实验验证表明，该学习得到的材料模型优于基于理论的模型及简单的曲线拟合方法。进一步用该模型优化膜片设计以应对不同举重任务，并与其它设计方案对比性能。这些工作推进了对该类执行器自然响应的理解，为单压力输入系统实现智能举升提供了可能。
原文摘要 · Abstract (English)
Soft pneumatic actuators (SPA) made from elastomeric materials can provide large strain and large force. The behavior of locally strain-restricted hyperelastic materials under inflation has been investigated thoroughly for shape reconfiguration, but requires further investigation for trajectories involving external force. In this work we model force-pressure-height relationships for a concentrically strain-limited class of soft pneumatic actuators and demonstrate the use of this model to design SPA response for object lifting. We predict relationships under different loadings by solving energy minimization equations and verify this theory by using an automated test rig to collect rich data for n=22 Ecoflex 00-30 membranes. We collect this data using an active learning pipeline to efficiently model the design space. We show that this learned material model outperforms the theory-based model and naive curve-fitting approaches. We use our model to optimize membrane design for different lift tasks and compare this performance to other designs. These contributions represent a step towards understanding the natural response for this class of actuator and embodying intelligent lifts in a single-pressure input actuator system.
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