Autonomous Robots

, Volume 37, Issue 4, pp 383–400 | Cite as

Approximate representations for multi-robot control policies that maximize mutual information

  • Benjamin CharrowEmail author
  • Vijay Kumar
  • Nathan Michael


We address the problem of controlling a small team of robots to estimate the location of a mobile target using non-linear range-only sensors. Our control law maximizes the mutual information between the team’s estimate and future measurements over a finite time horizon. Because the computations associated with such policies scale poorly with the number of robots, the time horizon associated with the policy, and typical non-parametric representations of the belief, we design approximate representations that enable real-time operation. The main contributions of this paper include the control policy, an algorithm for approximating the belief state, and an extensive study of the performance of these algorithms using simulations and real world experiments in complex, indoor environments.


Entropy Mutual Information Mobile Robot Gaussian Mixture Model Particle Filter 
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.



We gratefully acknowledge the support of ONR Grant N00014-07-1-0829, ARL Grant W911NF-08-2-0004, and AFOSR Grant FA9550-10-1-0567. Benjamin Charrow was supported by a NDSEG fellowship from the Department of Defense.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.University of PennsylvaniaPhiladelphiaUSA
  2. 2.Carnegie Mellon UniversityPittsburghUSA

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