Inflated-beam soft robots, such as tip-everting vine robots, can control curvature by contracting one beam side via pneumatic actuation. This work develops a general finite element modeling approach to characterize their bending. The model is validated across four pneumatic actuator types (series, compression, embedded, and fabric pneumatic artificial muscles), and can be extended to other designs. These actuators employ two bending mechanisms: geometry-based contraction and material-based contraction. The model accounts for intricate nonlinear effects of buckling and anisotropy. Experimental validation includes three working pressures (10, 20, and 30 kPa) for each actuator type. Geometry-based contraction yields significant deformation (92.1% accuracy) once the buckling pattern forms, reducing slightly to 80.7% accuracy at lower pressures due to stress singularities during buckling. Material-based contraction achieves smaller bending angles but remains at least 96.7% accurate. The open source models available at http://www.vinerobots.org support designing inflated-beam robots like tip-everting vine robots, contributing to waste reduction by optimizing designs based on material properties and stress distribution for effective bending and stress management.

翻译：充气梁软体机器人（如尖端外翻式藤蔓机器人）可通过气动方式收缩梁体一侧来实现曲率控制。本研究提出一种通用的有限元建模方法，用于表征其弯曲特性。该模型在四种气动驱动器类型（串联式、压缩式、内嵌式及织物式气动人工肌肉）上得到验证，并可拓展至其他设计。这些驱动器采用两类弯曲机制：基于几何结构的收缩与基于材料的收缩。模型考虑了屈曲与各向异性等复杂非线性效应。实验验证涵盖每种驱动器在三种工作压力（10、20及30 kPa）下的表现。基于几何结构的收缩在形成屈曲模式后会产生显著形变（准确率92.1%），但在低压条件下因屈曲过程中的应力奇异性，准确率略微下降至80.7%。基于材料的收缩可获得较小弯曲角度，但准确率始终不低于96.7%。开放源代码模型（详见http://www.vinerobots.org）可为尖端外翻式藤蔓机器人类充气梁机器人的设计提供支持，通过基于材料特性与应力分布优化设计方案，实现高效弯曲及应力管理，从而助力减少材料浪费。