Distributed Decision-Theoretic Active Perception for Multi-robot Active Information Gathering

  • Jennifer Renoux
  • Abdel-Illah Mouaddib
  • Simon LeGloannec
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8825)


Multirobot systems have made tremendous progress in exploration and surveillance. In that kind of problem, agents are not required to perform a given task but should gather as much information as possible. However, information gathering tasks usually remain passive. In this paper, we present a multirobot model for active information gathering. In this model, robots explore, assess the relevance, update their beliefs and communicate the appropriate information to relevant robots. To do so, we propose a distributed decision process where a robot maintains a belief matrix representing its beliefs and beliefs about the beliefs of the other robots. This decision process uses entropy and Kullback-Leibler in a reward function to access the relevance of their beliefs and the divergence with each other. This model allows the derivation of a policy for gathering information to make the entropy low and a communication policy to reduce the divergence. An experimental scenario has been developed for an indoor information gathering mission.


Multiagent System Markov Decision Process Information Gathering Belief State Reward Function 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Jennifer Renoux
    • 1
  • Abdel-Illah Mouaddib
    • 1
  • Simon LeGloannec
    • 2
  1. 1.University of CaenFrance
  2. 2.Airbus Defence and SpaceVal de ReuilFrance

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