Creation of a synthetic dataset for training precise movements of robots for in various industries - Gabdullin Rafael R.; Kugurakova Vlada V.; Iskhakov Robert T.; Kostyuk Daniil I. (BIO Web of Conferences)
Creating synthetic datasets for artificial intelligence training has a crucial role in modern developments. Considering the difficulties in collecting real data, which is often a costly and time-consuming process that requires significant resources and time. Synthetic data, on the other hand, allows generating large amounts of varied and controlled data that can be customized for specific training and testing needs. This makes the process of algorithm development and improvement more efficient and affordable. This paper presents a comprehensive tool for creating synthetic motion datasets based on rigging a 3D robot model. The ability to create and edit animations through the Blender interface is described. It supports a variety of well-known 3D model formats, providing flexibility in use, and includes powerful tools to achieve high-quality visual effects and realistic scenes. In addition, the tool can automatically generate a large number of robot images needed for training neural networks. By utilizing these capabilities, the tool greatly simplifies the creation of training datasets, making the process more efficient and affordable. Possible future enhancements include automation of rigging, further optimizing the functionality and usability of the tool for robotics and machine learning.