NODE – Navigation on Demand

Plug & Play – Orchestration and navigation solutions for mobile robots and AGVs

NODE – Navigation on Demand

Plug & Play – Orchestration and navigation solutions for mobile robots and AGVs

NODE – Navigation on Demand

Plug & Play – Orchestration and navigation solutions for mobile robots and AGVs

NODE – Navigation on Demand

NODE meets the three core requirements of mobile systems: flexibility, reliability and economy.

Free of infrastructure

NODE requires no markers or additional infrastructure. This eliminates the costs and effort involved in installing and maintaining them.

Simple commissioning

NODE works quickly and easily. With intuitive user interfaces and algorithms for self-configuration and self-optimization, new applications can be put into operation without the need for expert knowledge in the space of just a few hours.

Versatile use

NODE is flexible. Thanks to the high degree of autonomy and the ability to adapt to changing environmental conditions, it can be implemented for a wide range of applications. The desired vehicle behavior can be configured with ease and no additional infrastructure is required.

Cross-manufacturer fleets

NODE is not dependent on a specific manufacturer. Standardized interfaces and the fact that different vehicles types are supported means that models from different manufacturers can be operated in the same working area.

Technologies

NODE raises the operation and usability of mobile systems to a new level through the use of latest technologies.

Mobile robotics

By applying the latest research approaches and algorithms from mobile robotics, the degree of autonomy of the robots can be increased and the dependency on environmental conditions and required infrastructure can be reduced.

Networking and cooperation

By networking the vehicles with each other and with external computing resources, limited information horizons are resolved and a common database of the fleet is made possible. Cooperative navigation algorithms use this database for optimal control of the fleet.

Cloud-/Edge-Computing

By connecting to a cloud/edge infrastructure, compute-intensive processes can be outsourced and thus cost-intensive, local computing resources on the robots can be reduced. In addition, the software can be easily deployed and updated on the robots, and remote monitoring and analysis can be performed.

Machine Learning

By using current approaches to machine learning, the data collected by the fleet can be used to increase autonomy and efficiency of the robots and reduce manual setup effort.