Design and Analysis of Planar Linkage Mechanisms With Machine Learning and Other Computational Methods

Abstract

Planar linkage mechanisms, or linkages, are systems of rigid links and joints that translate an input motion into desirable output motions. In doing so, linkages enable us to perform complex tasks in fields such as manufacturing automation, robotics, computer graphics, and more with minimal input complexity. Recently, deep generative modeling solutions have been applied to generate linkage designs since these designs live in an intractable distribution; however, no scalable, conditional generative model has been found yet that can generate a set of optimal planar mechanical linkage designs, ranging in complexity, that best fit any type of generated path of motion by the user. In this work, we develop a generative flow network conditioned on linkage mechanism specifications to sample a diverse set of planar mechanisms. While developing this generative model, we also gain a much better understanding of the vast design space of linkages with linear and geometric algebra, graph neural networks, and implicit differentiation.