PneuGrasp: Computational Design of Soft Robots for State-Specific Grasping

Abstract Soft grippers, used in applications such as food handling and assistive devices, leverage multiple soft fluidic actuators (SFAs) for safe and compliant grasping. Designing SFAs is challenging because they must satisfy multiple functional requirements while operating outside the principles of rigid machine design, as they undergo large deformations and exhibit material nonlinearity. Because fabricating numerous design candidates is costly, computational tools have emerged to expedite the search for optimal designs. However, existing computational tools do not focus on SFA design optimization for state-specific grasping, where actuators are optimized for a particular deformation dictated by the intended use case. Moreover, many existing tools support a limited range of performance metrics and optimization modes. Here, we present PneuGrasp, an open-source tool for the design optimization of SFAs according to a user-specified grasping task. The tool can analyze design candidates across multi-functional combinations of seven performance metrics, including the understudied metrics of grasping force, bandwidth, and actuation energy. In addition, PneuGrasp supports three optimization modes that together provide parameter intuition and shorten optimization time. Through a series of examples evaluating over 1,000 design candidates, we demonstrate that PneuGrasp can identify optimized designs that outperform our baseline. For instance, one design achieved a 60 % reduction in maximum strain and a 52 % reduction in actuation volume, while another showed a 405 % decrease in a combined durability–grasping-force performance score. We fabricated and tested over 30 actuators across five distinct designs, demonstrating PneuGrasp's relative prediction capabilities.