Active Learning Design: Modeling Force Output for Axisymmetric Soft Pneumatic Actuators
This work addresses the challenge of designing efficient soft pneumatic actuators for lifting tasks, representing an incremental step towards intelligent single-pressure actuator systems.
The researchers tackled the problem of predicting force-pressure-height relationships for concentrically strain-limited soft pneumatic actuators to design object-lifting responses, resulting in a learned material model that outperformed theory-based and naive curve-fitting approaches, with verification using data from n=22 Ecoflex 00-30 membranes.
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.