A ChatGPT Framework for Mobility Analysis of Origami Mechanisms

Origami-inspired mechanisms have revolutionized robotic design by enabling lightweight, compact, and highly adaptable structures capable of complex motions and transformations. However, the internal presence of multiple closed-loop structures within origami mechanisms significantly complicates mobility analysis, making the calculation of their degrees of freedom particularly challenging. Common analytical methods, such as the Jacobian matrix approach, are often labor-intensive and require specialized expertise, limiting their practical application. To address this challenge, we propose a novel framework that leverages large language models (LLMs), specifically ChatGPT, to simplify and automate the mobility analysis of origami-inspired mechanisms. By training ChatGPT to understand and apply the Jacobian matrix method, the framework enables the automated generation of code for analyzing the mobility of various origami structures. The effectiveness of the proposed approach is demonstrated through three typical origami mechanisms: Miura, Waterbomb, and Kresling patterns. Results show that the framework achieves high reliability, with a success rate exceeding 95% under optimal conditions. This study not only pioneers the application of LLMs in the mobility analysis of origami mechanisms but also opens a new perspective for integrating AI-driven methods in solving complex mechanical problems.