Data augmentation for design of concentric tube continuum robots by generative adversarial networks

Abstract

Abstract Concentric tube continuum robots are a promising type of robot for various medical applications. Their application in neurosurgery poses challenging requirements for design and control that can be addressed by physics‐informed data‐based approaches. A prerequisite to data‐based modeling is an informative, rich data set. However, limited access to experimental data raises interest in partially or entirely synthetic data sets. In this contribution, we study the application of generative adversarial networks (GANs) for data augmentation in a data‐based design process of such robots. We propose a GAN framework suitable for curve‐fitting to generate synthetic trajectories of robots along with their corresponding control parameters. Our evaluation shows that the GANs can efficiently produce meaningful synthetic trajectories and control parameter pairs that show a good agreement with simulated trajectories.