Evolutionary Inverse Material Identification: Bespoke Characterization of Soft Materials Using a Metaheuristic Algorithm.

Michele Di Lecce,Pietro Valdastri,Peter Lloyd,Onaizah Onaizah,James H Chandler

doi:10.3389/frobt.2021.790571

Abstract

The growing interest in soft robotics has resulted in an increased demand for accurate and reliable material modelling. As soft robots experience high deformations, highly nonlinear behavior is possible. Several analytical models that are able to capture this nonlinear behavior have been proposed, however, accurately calibrating them for specific materials and applications can be challenging. Multiple experimental testbeds may be required for material characterization which can be expensive and cumbersome. In this work, we propose an alternative framework for parameter fitting established hyperelastic material models, with the aim of improving their utility in the modelling of soft continuum robots. We define a minimization problem to reduce fitting errors between a soft continuum robot deformed experimentally and its equivalent finite element simulation. The soft material is characterized using four commonly employed hyperelastic material models (Neo Hookean; Mooney–Rivlin; Yeoh; and Ogden). To meet the complexity of the defined problem, we use an evolutionary algorithm to navigate the search space and determine optimal parameters for a selected material model and a specific actuation method, naming this approach as Evolutionary Inverse Material Identification (EIMI). We test the proposed approach with a magnetically actuated soft robot by characterizing two polymers often employed in the field: Dragon Skin™ 10 MEDIUM and Ecoflex™ 00-50. To determine the goodness of the FEM simulation for a specific set of model parameters, we define a function that measures the distance between the mesh of the FEM simulation and the experimental data. Our characterization framework showed an improvement greater than 6% compared to conventional model fitting approaches at different strain ranges based on the benchmark defined. Furthermore, the low variability across the different models obtained using our approach demonstrates reduced dependence on model and strain-range selection, making it well suited to application-specific soft robot modelling.

Highlights

Over the last few decades, there has been growing interest in the field of soft robotics (Lipson, 2014; Rus and Tolley, 2015)
We propose Evolutionary Inverse Material Identification (EIMI), a material characterization approach aimed at identifying the parameters for a material based on the target application, in our case: Magnetically actuated soft robots (MASRs)
We presented an alternative method of material characterization to dynamically find the best material model based on the target application

Summary

Introduction

Over the last few decades, there has been growing interest in the field of soft robotics (Lipson, 2014; Rus and Tolley, 2015). These robots offer many advantages over their rigid body counterparts, with the ability to traverse complex trajectories to reach previously inaccessible areas, deform both actively and passively in multiple directions, and interact safely within delicate environments (e.g., with biological tissues) They often represent simpler fabrication and assembly with respect to rigid robots with joints; being molded in monolithic material designs (Chandler et al, 2020), with embedded strain limiting materials (Mosadegh et al, 2014; Polygerinos et al, 2015) or with the addition of functional components (e.g., magnetic particles) (Kim et al, 2019; Lloyd et al, 2019). On the other hand, offers the advantages of maintaining an entirely compliant device, introduces changes to the base elastomer material properties and characterization is limited to that specific matrix (da Veiga et al, 2021)

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