Review of soft fluidic actuators: classification and materials modeling analysis

Abstract

Soft actuators can be classified into five categories: tendon-driven actuators, electroactive polymers, shape-memory materials, soft fluidic actuators (SFAs), and hybrid actuators. The characteristics and potential challenges of each class are explained at the beginning of this review. Furthermore, recent advances especially focusing on SFAs are illustrated. There are already some impressive SFA designs to be found in the literature, constituting a fundamental basis for design and inspiration. The goal of this review is to address the latest innovative designs for SFAs and their challenges and improvements with respect to previous generations, and to help researchers to select appropriate materials for their application. We suggest seven influential designs: pneumatic artificial muscle, PneuNet, continuum arm, universal granular gripper, origami soft structure, vacuum-actuated muscle-inspired pneumatic, and hydraulically amplified self-healing electrostatic. The hybrid design of SFAs for improved functionality and shape controllability is also considered. Modeling SFAs, based on previous research, can be classified into three main groups: analytical methods, numerical methods, and model-free methods. We demonstrate the latest advances and potential challenges in each category. Regarding the fact that the performance of soft actuators is dependent on material selection, we then focus on the behaviors and mechanical properties of the various types of silicone that can be found in the SFA literature. For a better comparison of the different constitutive models of silicone materials proposed and tested in the literature, ABAQUS software is here employed to generate the engineering and true strain-stress data from the constitutive models, and compare them with standard uniaxial tensile test data based on ASTM412. Although the figures presented show that in a small range of stress–strain data, most of these models can predict the material model acceptably, few of them predict it accurately for large strain-stress values. Sensor technology integrated into SFAs is also being developed, and has the potential to increase controllability and observability by detecting a wide variety of data such as curvature, tactile contacts, produced force, and pressure values.