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

Abstract

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.