Efficient product creation processes are not possible without efficient, flexible and reliable material flow systems. AI - especially machine learning - enables significant progress in this field. The close integration of production and material flow requires flexibility on the part of the material flow systems, which can only be achieved via software-defined mobility - also in the in-plant (emission-free) and inter-plant areas. A test fleet of vehicles with excellent sensor technology and open software has an exceptional position internationally and makes it possible to open up new fields of research in these areas.

The aim of the project is to establish a distributed laboratory for automated guided vehicles (AGVs) to research the cross-site operation and networking of heterogeneous fleets of AGVs and to drive innovation. The main research areas include:

• Cross-site behaviour learning of heterogenous FTF fleets
• Intergrated routing of material transports across locations
• Exploratory learning of fleet control of heterogeneous FTF fleets

For this purpose, a fleet of driverless transport vehicles is to be procured, which will allow experimental investigations with a plurality of vehicles at one location and across locations.