REDUCED ORDER MODELLING OF SOFT ROBOTS

Abstract

Soft robots, prized for their adaptability, face modeling challenges due to their flexibility. The Finite Element Method (FEM), while accurate, is costly and time-consuming. Real-time FEM, though quicker, depends heavily on mesh quality. Learning-based methods bypass mechanics but demand extensive data and repetitive training. In this thesis, a new and efficient computational modeling paradigm is introduced. It is shown here that, for the most part, the isochoric (locally volume-preserving) constraint dominates behavior, and this can be built into closed-form kinematic deformation fields before even considering other aspects of constitutive modeling. This work, therefore, focuses on developing and applying primitive deformations that each primitive observes this locally volume-preserving constraint. By composing a wide enough variety of such deformations, many of the most common behaviors observed in soft robots can be replicated. This method is at least 50 times faster than the ABAQUS implementation of the finite element method (FEM) and has speed comparable with the real-time FEM framework SOFA. Experiments show that both the reduced order method and ABAQUS have approximately 10% error relative to experimentally measured displacements, as well as to each other. And the reduced order method outperforms SOFA when the deformation is highly nonlinear.