Automatic Path Planning for Industrial Welding Robots: Review and Introduction of a Novel Framework

Robotic welding is well established in large-scale enterprises whereby programming of the process follows a manual online teach-in method. This method implies immobilization of the robot cell during programming of a new product batch or adaptation of the program to products with geometric deviations. Small and medium-sized enterprises (SMEs) have to deal with much smaller lot sizes and high product variety leading to undesirably high programming time. Alternatively, using an offline programming (OLP) environment allows to improve productivity as the robot cell is uninterrupted during programming. OLP solutions further demand online adaptation of the path in a so-called after-teach-in process. Fraunhofer-IPA develops innovative OLP components to address both low programming time and after-teach-in requirements. In this paper, we first present a toolchain combining 3D sensing, cognitive functions and collision free path optimization. Then we present two user studies that illustrate the significant decrease in programming time allowed by implementation of the toolchain at SMEs. Finally, we briefly introduce machine learning based approaches currently explored for further automation and speed up of offline programming