Modeling a Sensorized Soft Layer for Adding Compliance to the Environment in Robotic Manipulation

Abstract

Soft robots are spreading quickly and widely thanks to their adaptability, tolerance to uncertainties, reliability, and intrinsic safety. To predict their behaviour and optimize their design, it is fundamental to devise models that account for the fact that they are made of highly non-linear materials and can be subjected to large, continuous deformations. In this work, we focus on modelling a soft robotic device developed to provide controllable compliance and sensing capabilities to the environment. Different models for non-linear materials in FEM static simulations are tested to evaluate which type of representation is more convenient in terms of accuracy.

Contact mechanics analysis of a soft robotic fingerpad

“…The fingers of the gripper were reproduced by 3D printing but a new element in silicone was added to simulate the human fingertip contact. The fingertip was made of EcoFlex 00-30 (Noor and Mahmud, 2015;Steck et al, 2019;Pozzi et al, 2021) and crafted using a 3D printed mould realized according to a proper design. After casting, the silicone fingerpad was mounted on the last phalanx of the WaveJoint finger (Figure 3).…”

“…For the study, a hyperelastic material was chosen for the silicone part, considering a geometric nonlinearity (Jindrich et al, 2003;Sergachev et al, 2019). The used hyperelastic model was the 2-parameter Mooney-Rivlin one which simulates the behavior of Ecoflex 00-30 silicone (Pozzi et al, 2021). The material properties are described with the shear modulus μ and two Mooney-Rivlin constants C 1 and C 2 , as reported in Table 1.…”

MOOSE-Based Finite Element Hyperelastic Modeling for Soft Robot Simulations

“…Work on FEM in soft robotics often tries to predict and optimize the design of soft actuators, as seen in [11]. More specifically, work has already been done using COMSOL-Multiphysics to model the nonlinear nature of soft sensors [12]. ANSYS was used to model hyperelastic material responses and additional soft sensors [13] and explore and optimize a variety of actuator parameters [14].…”