A Thermodynamic Approach to the Analysis of Multi-robot Cooperative Localization under Independent Errors

  • Yotam Elor
  • Alfred M. Bruckstein
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6234)


We propose a new approach to the simultaneous cooperative localization of a group of robots capable of sensing their own motion on the plane and the relative position of nearby robots. In the last decade, the use of distributed optimal Kalman filters (KF) to solve this problem have been studied extensively. In this paper, we propose to use a sub-optimal Kalman filter (denoted by EA). EA requires significantly less computation and communication resources then KF. Furthermore, in some cases, EA provides better localization.

In this paper EA is analyzed in a soft “thermodynamic” fashion i.e. relaxing assumptions are used during the analysis. The goal is not to derive hard lower or upper bounds but rather to characterize the robots expected behavior. In particular, to predict the expected localization error. The predictions were validated using simulations. We believe that this kind of analysis can be beneficial in many other cases.


Mobile Robot Kalman Filter Localization Error Main Diagonal Independent Error 
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-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Yotam Elor
    • 1
  • Alfred M. Bruckstein
    • 1
  1. 1.Faculty of Computer Science and the Goldstein UAV and Satellite CenterIsrael

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