A Computational Design Framework for Pressure-driven Soft Robots through Nonlinear Topology Optimization

In this work, we present a novel framework for synthesizing the design of pressure-driven soft robots. Contrary to traditional design methods, a topology optimization scheme is employed to find the optimal soft robotic structure given user-defined requirements. To our knowledge, the combination of pressure-driven topology optimization and soft robotics is still unexplored. Two difficulties are related to this problem. First, pressure-based topology optimization is challenging since the adaptive topology changes the pneumatic load at each optimization step. To deal with this issue, we exploit the facial connectivity in polygonal meshes to efficiently simulate the physics involving pneumatic actuation in soft robotics. The second issue is describing the hyperelastic nature of soft materials. Here, nonlinear finite element is explored such that large deformations can be described accurately. Numerical investigation shows that the framework can produce meaningful and insight-full material layouts with little to no prior knowledge of soft robotic design. This framework does not only accelerates design convergence, but it could also extend to the development of new and unexplored soft robot morphologies.