Elastomeric Strain Limitation for Design of Soft Pneumatic Actuators

TL;DR

This paper introduces strain-based design and control strategies for safe elastomeric soft pneumatic actuators, utilizing electroadhesive strain limiters and neural network modeling for precise shape and force control.

Contribution

It presents novel electroadhesive strain limiters and neural network models for trajectory control and shape reconfiguration in soft pneumatic actuators, enhancing safety and versatility.

Findings

Electroadhesive clutches enable rapid shape reorientation.

Encasing actuators in elastomeric sheaths allows variable inflation trajectories.

Neural networks accurately model pressure-trajectory relationships for control.

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.