From Simulation to Real Robots with Predictable Results: Methods and Examples

  • S. Balakirsky
  • S. Carpin
  • G. Dimitoglou
  • B. Balaguer


From a theoretical perspective, one may easily argue (as we will in this chapter) that simulation accelerates the algorithm development cycle. However, in practice many in the robotics development community share the sentiment that “Simulation is doomed to succeed” (Brooks, R., Matarić, M., Robot Learning, Kluwer Academic Press, Hingham, MA, 1993, p. 209). This comes in large part from the fact that many simulation systems are brittle; they do a fair-to-good job of simulating the expected, and fail to simulate the unexpected. It is the authors’ belief that a simulation system is only as good as its models, and that deficiencies in these models lead to the majority of these failures. This chapter will attempt to address these deficiencies by presenting a systematic methodology with examples for the development of both simulated mobility models and sensor models for use with one of today’s leading simulation engines. Techniques for using simulation for algorithm development leading to real-robot implementation will be presented, as well as opportunities for involvement in international robotics competitions based on these techniques.


Global Position System Global Position System Receiver Automate Guide Vehicle Simulation Platform Robotic Platform 
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.


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • S. Balakirsky
    • 1
  • S. Carpin
    • 2
  • G. Dimitoglou
    • 3
  • B. Balaguer
    • 2
  1. 1.National Institute of Standards and TechnologyGaithersburgUSA
  2. 2.University of CaliforniaMercedUSA
  3. 3.Hood CollegeFrederickUSA

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