  Computational design can excite the full potential of soft robotics that has
the drawbacks of being highly nonlinear from material, structure, and contact.
Up to date, enthusiastic research interests have been demonstrated for
individual soft fingers, but the frame design space (how each soft finger is
assembled) remains largely unexplored. Computationally design remains
challenging for the finger-based soft gripper to grip across multiple
geometrical-distinct object types successfully. Including the design space for
the gripper frame can bring huge difficulties for conventional optimisation
algorithms and fitness calculation methods due to the exponential growth of
high-dimensional design space. This work proposes an automated computational
design optimisation framework that generates gripper diversity to individually
grasp geometrically distinct object types based on a quality-diversity
approach. This work first discusses a significantly large design space (28
design parameters) for a finger-based soft gripper, including the
rarely-explored design space of finger arrangement that is converted to various
configurations to arrange individual soft fingers. Then, a contact-based Finite
Element Modelling (FEM) is proposed in SOFA to output high-fidelity grasping
data for fitness evaluation and feature measurements. Finally, diverse gripper
designs are obtained from the framework while considering features such as the
volume and workspace of grippers. This work bridges the gap of computationally
exploring the vast design space of finger-based soft grippers while grasping
large geometrically distinct object types with a simple control scheme.
