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Bayesian‐Based Domain Search Using Multiple Autonomous Vehicles with Intermittent Information Sharing
Author(s) -
Wang Yue,
Hussein Islam I.
Publication year - 2014
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.608
Subject(s) - domain (mathematical analysis) , bayesian probability , computer science , discretization , sensor fusion , binary number , artificial intelligence , object (grammar) , recursive bayesian estimation , state (computer science) , data mining , machine learning , algorithm , mathematics , mathematical analysis , arithmetic
This paper focuses on the development of Bayesian‐based domain search strategies using distributed multiple autonomous vehicles with intermittent communications. Multi‐sensor fusion based on observations from neighboring vehicles is implemented via binary Bayesian filtering. We prove theoretically that, under appropriate sensor models, the belief of whether there exists an object at every discretized cell in the domain will converge to the true state. An uncertainty map representing the entropies associated with these probabilities is constructed to guide the vehicles’ motion for domain exploration. Under the proposed strategies, all objects in the search domain will be detected. Different motion control schemes are numerically tested to confirm the effectiveness of the proposed search strategies.