Enabling Robust and Autonomous Materialhandling in Logistics Through Applied Deep Learning Algorithms

Abstract

In recent years, logistics costs in the automotive industry have risen significantly. One way to reduce these costs is to automate the entire material flow. To meet the flexible industrial challenges and dynamic changes, robots with intelligent perception are necessary. Such a perception algorithm is presented in the following. It consists of three modules. In the first module, all objects in the field of vision of the robot are detected, and their position is determined. Then the relevant objects for the respective process are selected. Finally, the gripping point of the next object to be handled is determined. By integrating the robots, it can be shown that by combining intelligent modules with pragmatic frame modules, automation in a challenging industrial environment is feasible.