Automated guided vehicles (AGVs) and mobile robots are a key element in establishing fast, reliable and cost-effective internal logistics. Artificial intelligence (AI) technologies such as machine learning or improved sensors and concepts for localizing vehicles can be used to implement new applications in production and logistics. Sharing environmental information via the cloud enables AGVs to organize themselves and plan optimal routes through factories and warehouses in a cooperative manner. In addition, installation space and payload can be optimized.

AI for production and logistics

At the technology forum, nine speakers from industry and research will present applications in industry and logistics, the current state of performance and the latest developments and research results on AGVs and mobile robotics. At the beginning, participants will gain an insight into the basics of AI and machine learning for industrial applications from Professor Marco Huber, who heads the Center for Cyber Cognitive Intelligence (CCI) at Fraunhofer IPA. Based on this, practical examples as well as explanations and outlooks on the latest technologies of autonomous systems and their diverse application possibilities in production, handling, logistics and service will be discussed.

Automotive industry moves forward

The automotive industry has always been a driver of new automation technologies. This also applies to the use of AGVs. Daimler AG summarizes the requirements that global players have of the market and suppliers and presents projects that have been implemented at Mercedes-Benz Cars. BMW will focus on the current degree of networking of autonomous transport robots and cloud technology. The presentation highlights the status of autonomy in BMW plants as well as cloud and networking development with associated platforms.

The presentation by Stefan Dörr, project manager at Fraunhofer IPA, will also focus on BMW's own "Smart Transport Robot" development. He talks about networking, cloud edge computing and machine learning as the basis for autonomous navigation. The aforementioned Smart Transport Robot serves as an application example. Using Fraunhofer IPA software, it navigates in a complex and changeable environment without any special infrastructure, even with a small amount of qualitatively different sensor data, which makes the application particularly flexible.