Determining the sizing of a protection system for a given shock environment is challenging. The sizing depends on several key factors, such as the mass of the payload, the maximum acceleration the payload can experience, the velocity of the system, and the mass of the actual system. This paper explores a method to determine the size of an icosahedral tensegrity system designed to protect a payload using a surrogate model of that system. The surrogate system is then tested iteratively using a genetic algorithm to determine the optimal system parameters. The surrogate model was validated to ensure that assumptions used to construct that model preserve the desired characteristics of the physical system. Once validated, a physical prototype was built to test an optimal condition solution that was calculated and was tested to ensure that the payload remained within the design constraints.

In this paper, we propose a new tractable ordinary differential equation formulation for dynamic simulation of fabric- reinforced inflatable soft robots. The method performs a lumped-parameter discretization of the continuum robot into discrete discs (inertia), spring elements, and threads (representing the inextensible fabric reinforcement). Using the repetition in the structure of the Lagrangian formulation of the dynamic equations of motion, a method is developed that outputs machine- readable analytical expressions for the equations of motion. The method does not require symbolic computation of derivatives. The recursive nature allows us to scale the model to an arbitrary number $N$ discs, and can represent buckling, twisting, and pleating that is commonly seen in very soft robots. The expressions generated were validated against manually-derived equations of motion for the two-disc case using both Lagrangian and Newton-Euler means. A simulation environment which parses and evaluates the analytical expressions generated at run-time was used to numerically integrate and predict the response of a four-disc example robot. Trajectories observed varied smoothly and plausibly predicted the behavior envisioned in robots like these.