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Soft pneumatic actuators (SPAs) produce motions for soft robots with simplepressure input, however they require to be appropriately designed to fit thetarget application. Available design methods employ kinematic models andoptimization to estimate the actuator response and the optimal designparameters, to achieve a target actuator's shape. Within SPAs, Bellow-SPAsexcel in rapid prototyping and large deformation, yet their kinematic modelsoften lack accuracy due to the geometry complexity and the materialnonlinearity. Furthermore, existing shape-matching algorithms are not providingan end-to-end solution from the desired shape to the actuator. In addition,despite the availability of computational design pipelines, an accessible anduser-friendly toolbox for direct application remains elusive. This paperaddresses these challenges, offering an end-to-end shape-matching designframework for bellow-SPAs to streamline the design process, and the open-sourcetoolbox SPADA (Soft Pneumatic Actuator Design frAmework) implementing theframework with a GUI for easy access. It provides a kinematic model grounded ona modular design to improve accuracy, Finite Element Method (FEM) simulations,and piecewise constant curvature (PCC) approximation. An Artificial NeuralNetwork-trained surrogate model, based on FEM simulation data, is trained forfast computation in optimization. A shape-matching algorithm, merging 3D PCCsegmentation and a surrogate model-based genetic algorithm, identifies optimalactuator design parameters for desired shapes. The toolbox, implementing theproposed design framework, has proven its end-to-end capability in designingactuators 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 deformableactuator.

SPADA: A Toolbox of Designing Soft Pneumatic Actuators for Shape Matching based on Surrogate Modeling