Implementation of an Intralogistics Routing-Service Basing on Decentralized Workspace Digitization


Article Preview

The paper presents an intralogistics routing-service for autonomous and versatile transport vehicles. An infrastructural sensor digitize the workspace of the vehicle and is the basis for the vehicle-specific routing plan. Nowadays, a central computing unit allocates transportation task to a known number of automated guided vehicles, which are usually of the same type. Furthermore, this device generates a routing appropriate to the dimensions and the kinematic gauge of the vehicle fleet. The pathing for each specific vehicle is calculated and the result is send to the different entities. The approach of this paper bases on the digitization of the workspace with a ceiling camera, which divides the scenery into moving obstacles and an adaptive background picture. A central computing unit receives the background picture of several cameras and stitch them together to an overview of the entire workspace, e.g. a production hall. Furthermore, the approach includes the development of automated guided vehicles to versatile autonomous vehicles, were each entity is able to calculate the pathing on a given routing plan. A fleet of versatile autonomous vehicles consists of vehicles with task-specific dimensions and kinematic gauges. Therefore, each vehicle needs its own routing-plan. The solution is that each vehicles uses a vehicle parameter-server and register itself with these parameters at the routing unit. This unit is calculating a routing-plan for each specific vehicle dimension and gauge and providing it. When getting a new task, the vehicles uses this routing-plan to do the pathing. The routing-algorithm is implemented inside the service-layer of the versatile autonomous vehicle system. This approach lowers the amount of data, which is send between the service layer and the transportation entities by reducing the information of the workspace to the possible routes of each specific vehicle. Furthermore, the calculation time for routing and pathing is lowered, because each vehicle is calculating its task-specific path, but the route-map is calculated once for each vehicle-type by the routing-service.



Edited by:

Jörg Franke, Michael Scholz and Annika Höft




M. Scholz et al., "Implementation of an Intralogistics Routing-Service Basing on Decentralized Workspace Digitization", Applied Mechanics and Materials, Vol. 882, pp. 90-95, 2018

Online since:

July 2018




* - Corresponding Author

[1] S.J. Hu, X. Zhu, H.Wang, Y. Koren, Product variety and manufacturing complexity in assembly systems and supply chains, CIRP Ann. Manuf. Techno. 57 (2008) 45-48.


[2] W. Eversheim, Organisation in der Produktionstechnik Grundlagen, 3rd ed., VDI-Verl., Düsseldorf (1996).

[3] M. Scholz, J. Franke, Bitte ein Taxi! Autonomer Materialtransport in der Intralogistik, handling (2015), 12:52-53.

[4] H. Martin, Transport- und Lagerlogistik: Planung, Struktur, Steuerung und Kosten von Systemen der Intralogistik, 9th ed., Springer Vieweg Wiesbaden (2014).

[5] F. Lütteke, Vielseitiges autonomes Transportsystem basierend auf Weltmodellerstellung mittels Datenfusion von Deckenkameras und Fahrzeugsensoren, Bamberg: Meisenbach, (2014).

[6] E. Kruse, F.M. Wahl, Camera-based observation of obstacle motions to derive statistical data for mobile robot motion planning, 1998 IEEE International Conference on Robotics and Automation (1998).


[7] M. Scholz, S. Rmasubramanian, A. Blank, S. Kreitlein, J. Franke, E|Flow II - Infrastructural Sensor Concepts to Digitize the Workspace for Sustainable and Resource Efficient Intralogistics, Applied Mechanics and Materials (2017).


[8] M. Brown, D.G. Lowe, Automatic Panoramic Image Stitching using Invariant Features, International Journal of Computer Vision (2007).

[9] H. Bay, A. Ess, T. Tuytelaars. L. van Gool, Speeded-Up Robust Features (SURF), Computer Vision and Image Understanding (2008).


[10] F. Karl, P. Schnellbach, G. Reinhart, J. Böhner, S. Freiberger, R. Steinhilper, S. Kreitlein, J. Franke, T. Maier, J. Pohl, and M. F. Zäh, Green Factory Bavaria Demonstrations-, Lehr- und Forschungsplattform zur Erhöhung der Energieeffizienz,, in wt Werkstatttechnik online.

[11] S. Kreitlein, A. Höft, S. Schwender, and J. Franke, Green Factories Bavaria: A Network of Distributed Learning Factories for Energy Efficient Production in 5th Conference on Learning Factories, p.58–63.