Towards Artificial Perception

  • André Dietrich
  • Sebastian Zug
  • Jörg Kaiser
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7613)


Adaptability to changing environments and environmental conditions is a key concern for future smart applications. Therefore, for autonomous systems it will be necessary to extend the local view on the environment with external sensors, either fixed or mobile ones. New evolving technologies support the acquisition of a myriad of information, described as “Internet of Things”, “Intelligent Environments”, “Industrial or Building Automation”, “Ambient Intelligence”, or “Ubiquitous/Pervasive Computing”, etc. Thus, information is always available, but its interpretation and integration into the own view remains an open problem. We therefore propose the development of a new type of distributed middleware for the environmental perception, that abstracts the environment from the diversity of available sensor systems. In three steps we describe how more and more functionalities can be extracted from the control application to support artificial perception and environment modelling.


artificial perception middleware environment model 


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  1. 1.
    Ikfast: The robot kinematics compiler (2012),
  2. 2.
    Wang, Y., Linnett, J., Roberts, J.: A unified approach to inverse and direct kinematics for four kinds of wheeled mobile robots and its applications. In: Proceedings of the 1996 IEEE International Conference on Robotics and Automation, vol. 4, pp. 3458–3465. IEEE (1996)Google Scholar
  3. 3.
    Şucan, I.A., Moll, M., Kavraki, L.E.: The Open Motion Planning Library. IEEE Robotics & Automation Magazine (to appear, 2012),
  4. 4.
    Schulze, M., Zug, S.: Exploiting the FAMOUSO Middleware in Multi-Robot Application Development with Matlab/Simulink. In: Proceedings of the ACM/IFIP/USENIX Middleware 2008 Conference Companion, Leuven, Belgium, pp. 74–77. ACM, New York (2008), CrossRefGoogle Scholar
  5. 5.
    Schulze, M.: Adaptierbare ereignisbasierte Middleware für ressourcenbeschränkte Systeme. Doktorarbeit, Fakultät für Informatik, Otto-von-Guericke Universität Magdeburg (2011)Google Scholar
  6. 6.
    Zug, S., Dietrich, A., Kaiser, J.: Fault-Handling in Networked Sensor Systems. Concept Press Ltd., St. Franklin (2012)Google Scholar
  7. 7.
    Zug, S., Dietrich, A.: Examination of Fusion Result Feedback for Fault-Tolerant and Distributed Sensor Systems. In: IEEE International Workshop on Robotic and Sensors Environments (ROSE 2010), Phoenix, AZ, USA (2010)Google Scholar
  8. 8.
    Zug, S.: Architektur für verteilte, fehlertolerante Sensor-Aktor-Systeme. Doktorarbeit, Fakultät für Informatik, Otto-von-Guericke Universität Magdeburg (2011)Google Scholar
  9. 9.
    Zug, S., Schulze, M., Dietrich, A., Kaiser, J.: Programming abstractions and middleware for building control systems as networks of smart sensors and actuators. In: Proceedings of Emerging Technologies in Factory Automation (ETFA 2010), Bilbao, Spain (September 2010)Google Scholar
  10. 10.
    Caulfield, H., Johnson, J.: Artificial perception and consciousness. In: Sixth International Conference on Education and Training in Optics and Photonics, Cancún, Mexico, July 28-30 1999, p. 112. Society of Photo Optical (2000)Google Scholar
  11. 11.
    Wills, L., Kannan, S., Sander, S., Guler, M., Heck, B., Prasad, J., Schrage, D., Vachtsevanos, G.: An open platform for reconfigurable control. IEEE Control Systems Magazine 21(3), 49–64 (2001)CrossRefGoogle Scholar
  12. 12.
    Hermann, A., Desel, J.: Driving situation analysis in automotive environment. In: IEEE International Conference on Vehicular Electronics and Safety, ICVES 2008, pp. 216–221. IEEE (2008)Google Scholar
  13. 13.
    Rotenstein, A., Rothenstein, A., Robinson, M., Tsotsos, J.: Robot middleware must support task-directed perception. In: Proc. ICRA 2nd Int. Workshop on Software Development and Integration into Robotics, Rome, Italy (2007)Google Scholar
  14. 14.
    Hähnel, D., Burgard, W., Thrun, S.: Learning compact 3d models of indoor and outdoor environments with a mobile robot. Robotics and Autonomous Systems 44(1), 15–27 (2003)CrossRefGoogle Scholar
  15. 15.
    Surmann, H., Nüchter, A., Hertzberg, J.: An autonomous mobile robot with a 3d laser range finder for 3d exploration and digitalization of indoor environments. Robotics and Autonomous Systems 45(3), 181–198 (2003)CrossRefGoogle Scholar
  16. 16.
    Rusu, R., Marton, Z., Blodow, N., Dolha, M., Beetz, M.: Towards 3d point cloud based object maps for household environments. Robotics and Autonomous Systems 56(11), 927–941 (2008)CrossRefGoogle Scholar
  17. 17.
    Rusu, R., Marton, Z., Blodow, N., Holzbach, A., Beetz, M.: Model-based and learned semantic object labeling in 3d point cloud maps of kitchen environments. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, pp. 3601–3608. IEEE (2009)Google Scholar
  18. 18.
    Roy, D., Hsiao, K., Mavridis, N.: Mental imagery for a conversational robot. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 34(3), 1374–1383 (2004)CrossRefGoogle Scholar
  19. 19.
    Hsiao, K., Mavridis, N., Roy, D.: Coupling perception and simulation: Steps towards conversational robotics. In: International Conference on Intelligent Robots and Systems, vol. 1, pp. 928–933. IEEE (October 2003)Google Scholar
  20. 20.
    Cook, D., Das, S.: How smart are our environments? an updated look at the state of the art. Pervasive and Mobile Computing 3(2), 53–73 (2007)CrossRefGoogle Scholar
  21. 21.
    Saffiotti, A., Broxvall, M., Seo, B., Cho, Y.: The peis-ecology project: a progress report. In: Proc. of the ICRA 2007 Workshop on Network Robot Systems, Rome, Italy, pp. 16–22. Citeseer (2007)Google Scholar
  22. 22.
    Smith, R.L.: The open dynamics engine (2007),
  23. 23.
    Meeussen, W., Hsu, J., Diankov, R.L.: URDF - Unified Robot Description Format (April 2012),
  24. 24.
    Diankov, R.: Automated construction of robotic manipulation programs. Ph.D. dissertation, Carnegie Mellon University, Robotics Institute (October 2010)Google Scholar
  25. 25.
    Dietrich, A., Zug, S., Kaiser, J.: Detecting External Measurement Disturbances Based on Statistical Analysis for Smart Sensors. In: Procedings of the IEEE International Symposium on Industrial Electronics (ISIE), pp. 2067–2072 (July 2010)Google Scholar
  26. 26.
    Dietrich, A., Zug, S., Kaiser, J.: Modelbasierte Fehlerdetektion in verteilten Sensor-Aktor-Systemen. In: 11./12. Forschungskolloquium am Fraunhofer IFF. Fraunhofer Institut für Fabrikbetrieb und Automatisierung, IFF (2011)Google Scholar
  27. 27.
    Dietrich, A., Zug, S., Kaiser, J.: Model based Decoupling of Perception and Processing. In: ERCIM/EWICS/Cyberphysical Systems Workshop, Resilient Systems, Robotics, Systems-of-Systems Challenges in Design, Validation & Verification and Certification, Naples, Italy (September 2011)Google Scholar
  28. 28.
    Zug, S., Schulze, M., Dietrich, A., Kaiser, J.: Reliable Fault-Tolerant Sensors for Distributed Systems. In: Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems (DEBS 2010), Cambridge, United Kingdom, pp. 105–106. ACM Press, New York (2010)CrossRefGoogle Scholar
  29. 29.
    Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.: Ros: an open-source robot operating system. In: ICRA Workshop on Open Source Software, vol. 3(3.2) (2009)Google Scholar
  30. 30.
    Foote, T., Marder-Eppstein, E., Meeussen, W.L.: tf - ros (April 2012),
  31. 31.
    Smith, R.C., Cheeseman, P.: On the Representation and Estimation of Spatial Uncertainty. The International Journal of Robotics Research 5(4), 56–68 (1986)CrossRefGoogle Scholar
  32. 32.
    Dietrich, A., Schulze, M., Zug, S., Kaiser, J.: Visualization of Robot’s Awareness and Perception. In: First International Workshop on Digital Engineering (IWDE), Magdeburg, Germany. ACM Press, New York (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • André Dietrich
    • 1
  • Sebastian Zug
    • 1
  • Jörg Kaiser
    • 1
  1. 1.Department of Distributed Systems (IVS)Otto-von-Guericke-Universität MagdeburgMagdeburgGermany

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