Cloud-based Cooperative Long-Term SLAM for mobile Robots in Industrial Applications.

In the course of changeable and highly flexible production, automated guided vehicles (AGVs) represent the key component for the realization of highly flexible and scalable intralogistics. In order to meet the high industrial requirements in terms of availability and precision, current solutions for navigation of the AGV usually require additional infrastructure of the environment or are limited to highly structured, predominantly static environments with low dynamics. Thus, they cannot meet the increasing demand for AGVs that operate flexibly and efficiently even in changing and dynamic industrial environments. In the course of this problem, this thesis investigates the use of networking and cloud computing technologies for the specific use case of navigation of mobile robots in an industrial context. The focus of the work within this topic is on the cooperative generation and updating of environment maps, which is a basic requirement for robust and precise localization and efficient path planning of the robots in these environments.