Increasing Reliability of Self-Folding of the Origami Hypar

Abstract Self-folding systems, which can transform autonomously from a flat sheet into a 3D machine, provide opportunities for rapidly fabricable robots that are deployable on-demand. Existing self-folding fabrication processes convert fold patterns into laminated structures that respond to external stimuli, most commonly heat. However, demonstrations of these approaches have been generally limited to simple fold patterns with little ambiguity in folding configuration, and the reliability of self-folding drops drastically with the fold pattern complexity. In this paper, we explore methods of biasing a symmetric fold pattern, the origami hyperbolic paraboloid (hypar), to fold into one of two possible configurations. The biasing methods are simulated using a bar-and-hinge inspired self-folding model and verified on physical samples. Based on these results, three techniques to bias the hypar by manipulating the target fold angles are proposed and tested. The results show that biasing a self-folding pattern can increase folding accuracy from 50% (purely random) to 70%, and provide insights for improving the reliability of future self-folding systems with complex fold patterns.