Speaker
Description
Soft robots have emerged as a subclass of continuum robots as a young research field in robotics. These soft robots are made from soft materials and can undergo large continuous deformations. Our study considers soft pneumatic actuators (SPA) with a slender cylindrical structure. The continuous deformations of soft robots are advantageous, however, the description of this behavior is highly non-linear and presents a vast possible design space. The design is typically driven by trial-and-error approaches, accompanied by simulations and experimental verification, furthermore, it is generally very task-specific. Machine Learning offers a promising way to enrich the design process of soft robots. It can restrict the initial design space and guide practitioners to good designs. In this talk, we show how Machine Learning can be used to support the inverse design process of slender soft robots. In our case, a workspace of the soft robot end-effector can be described at different pneumatic pressures. This workspace serves as the input for design optimization to obtain an optimal cross-section geometry for a slender soft robot. As this cross-section could be highly detailed in design, it is also necessary to favor manufacturable designs during the optimization stage.
