Self-adaptation for Mobile Robot Algorithms Using Organic Computing Principles

  • Jan Hartmann
  • Walter Stechele
  • Erik Maehle
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7767)


Many mobile robot algorithms require tedious tuning of parameters and are, then, often suitable to only a limited number of situations. Yet, as mobile robots are to be employed in various fields from industrial settings to our private homes, changes in the environment will occur frequently. Organic computing principles such as self-organization, self-adaptation, or self-healing can provide solutions to react to new situations, e.g. provide fault tolerance. We therefore propose a biologically inspired self-adaptation scheme to enable complex algorithms to adapt to different environments. The proposed scheme is implemented using the Organic Robot Control Architecture (ORCA) and Learning Classifier Tables (LCT). Preliminary experiments are performed using a graph-based Visual Simultaneous Localization and Mapping (SLAM) algorithm and a publicly available benchmark set, showing improvements in terms of runtime and accuracy.


Root Mean Square Error Mobile Robot Field Programmable Gate Array Robot Operating System Organic Computing 
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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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jan Hartmann
    • 1
  • Walter Stechele
    • 2
  • Erik Maehle
    • 1
  1. 1.Institute for Computer EngineeringUniversität zu LübeckGermany
  2. 2.Institute for Integrated SystemsTechnische Universität MünchenGermany

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