用椭球模型估算液驱膜结构变形，仅靠压力和液体量实现高精度状态感知。
A Generalized Modeling Approach to Liquid-driven Ballooning Membranes
- 以椭球近似描述膜的平面变形，结合压力与体积数据建模
- 实测误差仅0.80mm（占凹陷范围23%），力估误差0.15N（占测量范围10%）
- 无需外部传感器，适合无接触控制的柔性机器人应用
软体机器人正推动柔性材料在可重构系统中的应用。膜驱动型软体机器人通过受压可伸展膜实现稳定大变形，但其复杂变形动力学使控制与状态估计仍具挑战。本文提出一种液驱膨胀膜的通用建模方法，采用椭球近似来表征平面变形下的形状与拉伸。该方法仅依赖压力数据与可控液体体积的内在反馈，即可实现高精度膜状态估计。实验验证表明，对于基于膨胀膜的执行器，凹陷深度的均方根误差为 $RMSE_{h_2}=0.80\;$mm，相当于凹陷范围的23%，或未凹陷时执行器高度范围的6.67%。力估计的均方根误差为 $RMSE_{F}=0.15\;$N，约占测量力范围的10%。
原文摘要 · Abstract (English)
Soft robotics is advancing the use of flexible materials for adaptable robotic systems. Membrane-actuated soft robots address the limitations of traditional soft robots by using pressurized, extensible membranes to achieve stable, large deformations, yet control and state estimation remain challenging due to their complex deformation dynamics. This paper presents a novel modeling approach for liquid-driven ballooning membranes, employing an ellipsoid approximation to model shape and stretch under planar deformation. Relying solely on intrinsic feedback from pressure data and controlled liquid volume, this approach enables accurate membrane state estimation. We demonstrate the effectiveness of the proposed model for ballooning membrane-based actuators by experimental validation, obtaining the indentation depth error of $RMSE_{h_2}=0.80\;$mm, which is $23\%$ of the indentation range and $6.67\%$ of the unindented actuator height range. For the force estimation, the error range is obtained to be $RMSE_{F}=0.15\;$N which is $10\%$ of the measured force range.
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