Mobile Robots in Smart Environments: The Current Situation

  • Michael Arndt
  • Karsten Berns
Conference paper
Part of the Informatik aktuell book series (INFORMAT)


This work aims to give an overview about the current research concerning mobile robots in smart environments. It is part of the motivation of a paradigm that aims to shift complexity away from mobile machines into the (smart) environment without sacrificing the safety of the overall system. Several results of current and ongoing research will be presented, including a powerful simulation environment that allows to simulate the overall system including ambient sensors, communication within a wireless sensor network (WSN) and mobile robots. Also some experiments about the localization of mobile robots in smart environments and results will be presented.


Wireless Sensor Network Mobile Robot Receive Signal Strength Mobile Robotic Smart Environment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Arndt, M., Berns, K.: Optimized mobile indoor robot navigation through probabilistic tracking of people in a wireless sensor network. In: Proceedings of the 7th German Conference on Robotics (Robotik 2012), pp. 355–360. Munich, 21–22 May 2012Google Scholar
  2. Bahl, P., Padmanabhan, V.N.: Radar: an in-building rf-based user location and tracking system. In: Proceedings of the 19th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM 2000), vol. 2, pp. 775–784 (2000)Google Scholar
  3. Braun, T., Wettach, J., Berns, K.: A customizable, multi-host simulation and visualization framework for robot applications. In: 13th International Conference on Advanced Robotics (ICAR07), pp. 1105–1110. Jeju, 21–24 August 2007Google Scholar
  4. Coradeschi, S., Saffiotti, A.: Symbiotic robotic systems: humans, robots, and smart environments. IEEE Intell. Syst. 21(3), 82–84 (2006)CrossRefGoogle Scholar
  5. Harper, R.: Inside the Smart Home. Springer, London (2003)CrossRefGoogle Scholar
  6. Honkavirta, V., Perala, T., Ali-Loytty, S., Piche, R.: A comparative survey of wlan location fingerprinting methods. In: 6th Workshop on Positioning, Navigation and Communication (WPNC 2009), pp. 243–251. March 2009Google Scholar
  7. Mao, G., Fidan, B., Anderson, B.D.O.: Wireless sensor network localization techniques. Comput. Netw. 51(10), 2529–2553 (2007)zbMATHCrossRefGoogle Scholar
  8. Parsons, J.D.: The Mobile Radio Propagation Channel. Pentech, London (1992)Google Scholar
  9. Saffiotti, A., Broxvall, M.: PEIS ecologies: Ambient intelligence meets autonomous robotics. In: Proceedings of the International Conference on Smart Objects and Ambient Intelligence (sOc-EUSAI), pp. 275–280. Grenoble (2005)Google Scholar
  10. Saffiotti, A., Broxvall, M., Gritti, M., LeBlanc, K., Lundh, R., Rashid, J., Seo, B.S., Cho, Y.J.: The PEIS-ecology project: vision and results. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2329–2335. Nice, Sept. 2008Google Scholar
  11. Shin, J., Han, D.: Multi-classifier for wlan fingerprint-based positioning system. In: Proceedings of the World Congress on, Engineering, vol. 1 (2010)Google Scholar
  12. Sanfeliu, A., Hagita, N., Saffiotti, A.: Network robot systems. Robot. Auton. Syst. 56(10), 793–797 (2008)CrossRefGoogle Scholar
  13. Wille, S., Wehn, N., Martinovic, I., Kunz, S., Göhner, P.: Amica-design and implementation of a flexible, compact and low-power node platform. Technical report, University of Kaiserslautern, Oct 2010.
  14. Zuehlke, D.: Smartfactory-from vision to reality in factory technologies. In: Chung M.J., Misra, P. (eds.) Proceedings of the 17th IFAC World Congress, pp. 82–89 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Robotics Research Lab, Department of Computer SciencesUniversity of KaiserslauternKaiserslauternGermany

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