NAiSE GmbH

Indoor Positioning Systems

Founded in 2017 by

  • Jens Heinrich - Kai Przybysz

  • Robert Libert - Felicitas Knapp

Incubation period

03-07-2017 to 31-07-2018

About NAiSE GmbH

The technology startup NAiSE develops indoor navigation systems based on GPS and UWB (ultra-wideband) and is primarily focused on the German market of adaptive logistics. With two electrical engineers, Robert Libert and Kai Przybysz, the IT business engineer Jens Heinrich and marketing expert Felicitas Knapp, the diversified team is ready to hit the ground running.

Contact info

The challenge

Today's AGV are mostly operated by inductive or optical track guidance. If you imagine that a person could only move on predefined lines, it becomes clear that such systems are not very efficient. And since complex structural changes are associated with these solutions, they are, e. g. in the case of the route change, inflexible and usually accompanied by high costs. Camera- or laser-based systems are also very expensive and cannot take dynamic, short-term changes in space (storage of crates, people on the route) into account. There are already prototypes from the track guidance-free transport systems, which, however, are usually controlled manually.

The solution

In the context of a Smart Factory the NAiSE system enables positioning, tracking, data analysis and eventually navigation of automated guided vehicles (AGV). There is no track guidance needed, what means an absolute and real-time determination of positioning. This way AGV gain much more efficiency and flexibility. Therefore the NAiSE hardware system, which look similar to WiFi Access Points, needs to be permanently installed in the room. The position data, which we gain by the tracking, are processed and visualized locally in real time. The collected data is passed on to our cloud-based analysis software. These analysis can be used to identify bottlenecks by means of statistical evaluations (e. g. in the form of heatmaps and diagrams). At the same time, the data are used in a learning cycle to optimize the routing strategies of the AGV and to realize an autonomous navigation.