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- W44854559 abstract "This paper presents a scalable information theoreticapproach to infer the state of an environment by distributivelycontrolling robots equipped with sensors. The robots iterativelyestimate the environment state using a recursive Bayesian filter,while continuously moving to improve the quality of the estimateby following the gradient of mutual information. Both the filterand the controller use a novel algorithm for approximatingthe robots’ joint measurement probabilities, which combinesconsensus (for decentralization) and sampling (for scalability).The approximations are shown to approach the true jointmeasurement probabilities as the size of the consensus roundsgrows or as the network becomes complete. The resultinggradient controller runs in constant time with respect to thenumber of robots, and linear time with respect to the numberof sensor measurements and environment discretization cells,while traditional mutual information methods are exponentialin all of these quantities. Furthermore, the controller is provento be convergent between consensus rounds and, under certainconditions, is locally optimal. The complete distributed inferenceand coordination algorithm is demonstrated in experiments withfive quad-rotor flying robots and simulations with 100 robots." @default.
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- W44854559 date "2011-09-01" @default.
- W44854559 modified "2023-09-27" @default.
- W44854559 title "A Scalable Information Theoretic Approach to Multi-robot Coordination" @default.
- W44854559 hasPublicationYear "2011" @default.
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