Multi-material inverse design of soft deformable bodies via functional optimization


 Controlling the deformation of a soft body has potential applications in ﬁelds requiring precise control over the shape of the body. Areas such as medical robotics can use the shape control of soft robots to repair aneurysms in humans, deliver medicines within the body, among other applications. However, given known external loading, it is usually not possible to deform a soft body into arbitrary shapes if it is fabricated using only a single material. In this work, we propose a new physics-based method for the computational design of soft hyperelastic bodies to address this problem. The method takes as input an undeformed shape of a body, a speciﬁed external load, and a user desired ﬁnal shape. It then solves an inverse problem in design using nonlinear optimization subject to physics constraints. The nonlinear program is solved using a gradient-based interior-point method. Analytical gradients are computed for eﬃciency. The method outputs ﬁelds of material properties which can be used to fabricate a soft body. A body fabricated to match this material ﬁeld is expected to deform into a user-desired shape, given the same external loading input. Two regularizers are used to ascribe a priori characteristics of smoothness and contrast, respectively, to the spatial distribution of material ﬁelds. The performance of the method is tested on three example cases in silico.