An Intelligent Pipeline for Localization of Industrial Components in Robotic Manufacturing Applications

The rising skill shortage problem in Europe threatens the economic slowdown in the manufacturing sector. Approaches based on artificial intelligence can play a crucial role in bridging the shortage gap if they can be integrated into robot-assisted production to simplify repetitive manual tasks. Localizing components in a production cell is a familiar problem of robot-assisted production. Robots are often taught trajectories manually, which requires expertise in robot programming. Some of the existing feature-based computer vision solutions can localize a component in 3D space. However, these solutions are not versatile enough to be integrated across different components and production cells. This paper proposes an AI-based solution in the form of a pipeline for the 6D localization of components that can be integrated into multiple industrial use cases. The pipeline encompasses flows for generating synthetic images of components from their CAD model, training deep neural networks to estimate component poses, and improving their accuracy for manufacturing applications. The performance of the pipeline has been validated for components in a production-related environment. The paper also demonstrates the versatility of the pipeline by deploying it for a robotic spray coating use case. Such AI skills can empower the skilled workforce on the shop floor so that they can focus on the overall manufacturing process.