Good Experimental Methodologies and Simulation in Autonomous Mobile Robotics

  • Francesco Amigoni
  • Viola Schiaffonati
Part of the Studies in Computational Intelligence book series (SCI, volume 314)


Experiments have proved fundamental constituents for natural sciences and it is reasonable to expect that they can play a useful role also in engineering, for example when the behavior of an artifact and its performance are difficult to characterize analytically, as it is often the case in autonomous mobile robotics. Although their importance, experimental activities in this field are often carried out with low standards of methodological rigor. Along with some initial attempts to define good experimental methodologies, the role of simulation experiments has grown in the last years, as they are increasingly used instead of experiments with real robots and are now considered as a good tool to validate autonomous robotic systems. In this work, we aim at investigating simulations in autonomous mobile robotics and their role in experimental activities conducted in the field.


Mobile Robot Experimental Activity Robotic System Autonomous Robot Real Robot 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Amigoni, F., Gasparini, S., Gini, M.: Good experimental methodologies for robotic mapping: A proposal. In: IEEE Int’l Conf. on Robotics and Automation, pp. 4176–4181 (2007)Google Scholar
  2. 2.
    Amigoni, F., Reggiani, M., Schiaffonati, V.: An insightful comparison between experiments in mobile robotics and in science. Autonomous Robots 27(4), 313–325 (2009)CrossRefGoogle Scholar
  3. 3.
    Carpin, S., Lewis, M., Wang, J., Balakirsky, S., Scrapper, C.: Bridging the gap between simulation and reality in urban search and rescue. In: Lakemeyer, G., Sklar, E., Sorrenti, D.G., Takahashi, T. (eds.) RoboCup 2006: Robot Soccer World Cup X. LNCS (LNAI), vol. 4434, pp. 1–12. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
  5. 5.
    Frigg, R., Hartmann, S.: Models in science. In: Zalta, E.N. (ed.) The Stanford Encyclopedia of Philosophy (2009), (Summer 2009)
  6. 6.
    Hacking, I.: Representing and Intervening. Introductory Topics in the Philosophy of Natural Science. Cambridge University Press, Cambridge (1983)Google Scholar
  7. 7.
    Hartmann, S.: The world as a process: Simulations in the natural and social sciences. In: Hegselmann, R., et al. (eds.) Simulation and Modeling in the Social Sciences from the Philosophy of Science Point of View, pp. 77–100. Kluwer, Dordrecht (1996)Google Scholar
  8. 8.
    Howard, A., Roy, N.: The robotics data set repository, radish (2003),
  9. 9.
    Humphreys, P.: Extending Ourselves. Computational Science, Empiricism, and Scientific Method. Oxford University Press, Oxford (2004)Google Scholar
  10. 10.
    Johnson, D.: A Theoretician’s Guide to the Experimental Analysis of Algorithms. In: Goldwasser, M.H., Johnson, D.S., McGeoch, C.C. (eds.) Data Structures, Near Neighbor Searches, and Methodology: Fifth and Sixth DIMACS Implementation Challenges, pp. 215–250. American Mathematical Society, Providence (2002)Google Scholar
  11. 11.
    Kitcher, P.: Real realism: The Galileian strategy. Philosophical Review 110, 151–197 (2001)Google Scholar
  12. 12.
    LaValle, S.: Planning Algorithms. Cambridge University Press, Cambridge (2006)zbMATHCrossRefGoogle Scholar
  13. 13.
    Madhavan, R., Scrapper, C., Kleiner, A.: Special issue on characterizing mobile robot localization and mapping. Autonomous Robots 27(4), 309–481 (2009)CrossRefGoogle Scholar
  14. 14.
    Naylor, T.: Computer Simulation Techniques. John Wiley, Chichester (1966)Google Scholar
  15. 15.
  16. 16.
    Player Project (2009),
  17. 17.
  18. 18.
    Rohrlich, F.: Computer simulation in the physical sciences. In: Fine, A., Forbes, M., Wessels, L. (eds.) Proc. 1990 Biennal Meeting of the Philosophy of Science Association, pp. 145–163 (1991)Google Scholar
  19. 19.
    RoSta: Robot standards and reference architectures (2007),
  20. 20.
    Siegwart, R., Nourbakhsh, I.: Autonomous Mobile Robotics. MIT Press, Cambridge (2004)Google Scholar
  21. 21.
    Simon, H.: The Sciences of the Artificial. MIT Press, Cambridge (1969)Google Scholar
  22. 22.
    Simpson, J.: Simulations are not models. In: Models and Simulations Conference, vol. 1 (2006)Google Scholar
  23. 23.
    Stachniss, C., Frese, U., Grisetti, G.: (2007),
  24. 24.
    The RoboCup Federation: RoboCup (1998),
  25. 25.
  26. 26.
    Winsberg, E.: Simulated experiments: Methodology for virtual world. Philosophy of Science 70, 105–125 (2003)CrossRefGoogle Scholar
  27. 27.
    Winsberg, E.: Models of success vs. success of models: Reliability without truth. Synthese 152(1), 1–19 (2006)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Francesco Amigoni
    • 1
  • Viola Schiaffonati
    • 1
  1. 1.Artificial Intelligence and Robotics Laboratory, Dipartimento di Elettronica e InformazionePolitecnico di MilanoItaly

Personalised recommendations