  Soft pneumatic actuators (SPAs) produce motions for soft robots with simple
pressure input, however they require to be appropriately designed to fit the
target application. Available design methods employ kinematic models and
optimization to estimate the actuator response and the optimal design
parameters, to achieve a target actuator's shape. Within SPAs, Bellow-SPAs
excel in rapid prototyping and large deformation, yet their kinematic models
often lack accuracy due to the geometry complexity and the material
nonlinearity. Furthermore, existing shape-matching algorithms are not providing
an end-to-end solution from the desired shape to the actuator. In addition,
despite the availability of computational design pipelines, an accessible and
user-friendly toolbox for direct application remains elusive. This paper
addresses these challenges, offering an end-to-end shape-matching design
framework for bellow-SPAs to streamline the design process, and the open-source
toolbox SPADA (Soft Pneumatic Actuator Design frAmework) implementing the
framework with a GUI for easy access. It provides a kinematic model grounded on
a modular design to improve accuracy, Finite Element Method (FEM) simulations,
and piecewise constant curvature (PCC) approximation. An Artificial Neural
Network-trained surrogate model, based on FEM simulation data, is trained for
fast computation in optimization. A shape-matching algorithm, merging 3D PCC
segmentation and a surrogate model-based genetic algorithm, identifies optimal
actuator design parameters for desired shapes. The toolbox, implementing the
proposed design framework, has proven its end-to-end capability in designing
actuators to precisely match 2D shapes with root-mean-square errors of 4.16,
2.70, and 2.51mm, and demonstrating its potential by designing a 3D deformable
actuator.
