Global Localization and Position Tracking of Autonomous Transport Vehicles

  • Christof Röhrig
  • Christopher Kirsch
  • Julian Lategahn
  • Marcel Müller
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 186)


This chapter presents global localization and position tracking for a swarm of autonomous transport vehicles which transport Euro-bins in a distribution center or warehouse. Localization is realized by sensor fusion of range measurements obtained from an IEEE 802.15.4a network and laser range finders. The IEEE 802.15.4a network is used for communication as well as for global localization. Laser range finders are used to detect landmarks and to provide accurate positioning for docking maneuvers. Range measurements are fused in a Monte Carlo Particle Filter. The chapter presents the design of the global localization and position tracking algorithms. Experimental results are given to prove the effectiveness of the proposed methods.


Localization IEEE 802.15.4a CSS Autonomous transport vehicle Mobile robot Automated guided vehicle  AGV Swarm intelligence 



This work was supported by the Ministry of Innovation, Science and Research of the German State of North Rhine-Westphalia (FH-Extra, grant number 29 00 130 02/12) and the European Union Fonds for Regional Development (EFRE). Furthermore the project was financially supported by Nanotron Technologies GmbH in Berlin, Germany and the University of Applied Sciences and Arts in Dortmund (HIFF, project number 04 001 79).


  1. 1.
    Alriksson P, Rantzer A (2007) Experimental evaluation of a distributed kalman filter algorithm. In: Proceedings of the 46th IEEE conference on decision and control. New Orleans, pp 5499–5504Google Scholar
  2. 2.
    Baggio A, Langendeon K (2006) Monte-Carlo localization for mobile wireless sensor networks. Technology report of Delft University (PDS: 2006–004)Google Scholar
  3. 3.
    Bahl P, Padmanabhan VN (2000) RADAR: an in-building RF-based user location and tracking system. Proceedings of the 19th annual joint conference of the IEEE computer and communications societies, vol 2. Tel Aviv, Israel, pp 775–784Google Scholar
  4. 4.
    Dellaert F, Fox D, Burgard W, Thrun S (1999) Monte Carlo localization for mobile robots. In: Proceedings of the IEEE international conference on robotics and automation (ICRA99)Google Scholar
  5. 5.
    Fernández-Madrigal JA, Cruz E, González J, Galindo C, Blanco JL (2007) Application of UWB and GPS technologies for vehicle localization in combined indoor-outdoor environments. In: Proceedings of the international symposium on signal processing and its applications. Sharja, United Arab EmiratesGoogle Scholar
  6. 6.
    Gezici S, Tian Z, Giannakis GB, Kobayashi H, Molisch AF, Poor HV, Sahinoglu Z (2005) Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks. Signal Process Mag 22(4):70–84CrossRefGoogle Scholar
  7. 7.
    Kamagaew A, Stenzel J, Nettsträter A, Ten Hompel M (2011) Concept of cellular transport systems in facility logistics. In: Proceedings of the 5th international conference on automation, robots and applications (ICARA 2011)Google Scholar
  8. 8.
    Moore D, Leonard J, Rus D, Teller S (2004) Robust distributed network localization with noisy range measurements. Proceedings of the 2nd international conference on embedded networked sensor systems. Baltimore, USA, pp 50–61Google Scholar
  9. 9.
    Patwari N, Hero AO, Perkins M, Correal NS, O’Dea R (2003) Relative location estimation in wireless sensor networks. IEEE Trans Signal Process 51(8):2137–2148CrossRefGoogle Scholar
  10. 10.
    Priyantha NB, Miu AKL, Balakrishnan H, Teller S (2001) The cricket compass for context-aware mobile applications. Proceedings of the 7th annual international conference on mobile computing and networking. Rome, Italy, pp 1–14Google Scholar
  11. 11.
    Röhrig C, Heß D, Kirsch C, Künemund F (2010) Localization of an omnidirectional transport robot using IEEE 802.15.4a ranging and laser range finder. In: Proceedings of the 2010 IEEE/RSJ international conference on intelligent robots and systems (IROS 2010). Taipei, Taiwan, pp 3798–3803Google Scholar
  12. 12.
    Röhrig C, Künemund F (2007) Estimation of position and orientation of mobile systems in a wireless LAN. In: Proceedings of the 46th IEEE conference on decision and control. New Orleans, USA, pp 4932–4937Google Scholar
  13. 13.
    Röhrig C, Lategahn J, Müller M, Telle L (2012) Global localization for a swarm of autonomous transport vehicles using IEEE 802.15.4a CSS. In: Lecture notes in engineering and computer science: proceedings of the international multiconference of engineers and computer scientists, (IMECS 2012). Hong Kong, pp 828–833Google Scholar
  14. 14.
    Sahinoglu Z, Gezici S (2006) Ranging in the IEEE 802.15.4a standard. In: Proceedings of the IEEE annual wireless and microwave technology conference (WAMICON ’06). Clearwater, Florida, USA, pp 1–5Google Scholar
  15. 15.
    Thrun S, Burgard W, Fox D (2005) Probabilistic robotics. MIT press, CambridgeGoogle Scholar
  16. 16.
    Vossiek M, Wiebking L, Gulden P, Wieghardt J, Hoffmann C, Heide P (2003) Wireless local positioning. Microw. Mag 4(4):77–86CrossRefGoogle Scholar
  17. 17.
    Wurman PR, D’Andrea R, Mountz M (2008) Coordinating hundreds of cooperative, autonomous vehicles in warehouses. AI Mag. 29(1):9–19Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Christof Röhrig
    • 1
  • Christopher Kirsch
    • 1
  • Julian Lategahn
    • 1
  • Marcel Müller
    • 1
  1. 1.Intelligent Mobile Systems LabUniversity of Applied Sciences and Arts in DortmundDortmundGermany

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