Inverse Design of Planar Clamped-Free Elastic Rods from Noisy Data

TL;DR

This paper develops a robust inverse design framework for determining the natural shape of planar rods that deform into desired shapes, accounting for data noise and uncertainties, with applications in soft robotics and morphing structures.

Contribution

It introduces a combined theoretical and learning-based approach to inverse rod design that effectively handles uncertainties and improves robustness over previous methods.

Findings

The framework accurately predicts natural shapes from noisy data.

Combining statics with the adjoint method enhances robustness.

Numerical validation confirms improved accuracy and noise resilience.

Abstract

Slender structures, such as rods, often exhibit large nonlinear geometrical deformations even under moderate external forces (e.g., gravity). This characteristic results in a rich variety of morphological changes, making them appealing for engineering design and applications, such as soft robots, submarine cables, decorative knots, and more. Prior studies have demonstrated that the natural shape of a rod significantly influences its deformed geometry. Consequently, the natural shape of the rod should be considered when manufacturing and designing rod-like structures. Here, we focus on an inverse problem: can we determine the natural shape of a suspended 2D planar rod so that it deforms into a desired target shape? We begin by formulating a theoretical framework based on the statics of planar rod equilibrium that can compute the natural shape of a planar rod given its target shape. Furthermore, we analyze the impact of uncertainties (e.g., noise in the data) on the accuracy of the theoretical framework. The results reveal the shortcomings of the theoretical framework in handling uncertainties in the inverse problem, a fact often overlooked in previous works. To mitigate the influence of the uncertainties, we combine the statics of the planar rod with the adjoint method for parameter sensitivity analysis, constructing a learning framework that can efficiently explore the natural shape of the designed rod with enhanced robustness. This framework is validated numerically for its accuracy and robustness, offering valuable insights into the inverse design of soft structures for various applications, including soft robotics and animation of morphing structures.