Elastomeric Strain Limitation for Design of Soft Pneumatic Actuators

• URL: http://arxiv.org/abs/2604.02609v1
• Date: Fri, 03 Apr 2026 00:56:14 GMT
• Title: Elastomeric Strain Limitation for Design of Soft Pneumatic Actuators
• Abstract summary: This thesis presents strategies for the design, modeling, and strain-based control of human-safe elastomeric soft pneumatic actuators.<n>We investigate electroadhesive (EA) strain limiters for variable shape generation, rapid force application, and targeted inflation trajectories.<n>We validate theoretical models based on material properties and energy minimization using active learning and automated testing.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Modern robots embody power and precision control. Yet, as robots undertake tasks that apply forces on humans, this power brings risk of injury. Soft robotic actuators use deformation to produce smooth, continuous motions and conform to delicate objects while imparting forces capable of safely pushing humans. This thesis presents strategies for the design, modeling, and strain-based control of human-safe elastomeric soft pneumatic actuators (SPA) for force generation, focusing on embodied mechanical response to simple pressure inputs. We investigate electroadhesive (EA) strain limiters for variable shape generation, rapid force application, and targeted inflation trajectories. We attach EA clutches to a concentrically strain-limited elastomeric membrane to alter the inflation trajectory and rapidly reorient the inflated shape. We expand the capabilities of EA for soft robots by encasing them in elastomeric sheaths and varying their activation in real time, demonstrating applications in variable trajectory inflation under identical pressure sweeps. We then address the problem of trajectory control in the presence of external forces by modeling the pressure-trajectory relationship for a concentrically strain-limited class of silicone actuators. We validate theoretical models based on material properties and energy minimization using active learning and automated testing. We apply our ensemble of neural networks for inverse membrane design, specifying quasi-static mass lift trajectories from a simple pressure sweep. Finally, we demonstrate the power of multiple pressure-linked actuators in a proof-of-concept mannequin leg lift.

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