We propose an anchorless distributed technique for estimating the centroid of a network of agents from noisyrelativemeasurements. The positions of the agents are then obtained relative to the estimated centroid. The usual approach to multi-agent localization assumes instead that one anchor agent exists in the network, and the other agents’ positions are estimated with respect to the anchor. We show that our centroid-based algorithm converges to the optimal solution, and such a centroid-based representation produces results that are more accurate than anchor-based ones, irrespective of the selected anchor.

Distributed Centroid Estimation from Noisy Relative Measurements / Aragues, R.; Carlone, Luca; Sagues, C.; Calafiore, Giuseppe Carlo. - In: SYSTEMS & CONTROL LETTERS. - ISSN 0167-6911. - STAMPA. - 61:7(2012), pp. 773-779. [10.1016/j.sysconle.2012.04.008]

Distributed Centroid Estimation from Noisy Relative Measurements

CARLONE, LUCA;CALAFIORE, Giuseppe Carlo
2012

Abstract

We propose an anchorless distributed technique for estimating the centroid of a network of agents from noisyrelativemeasurements. The positions of the agents are then obtained relative to the estimated centroid. The usual approach to multi-agent localization assumes instead that one anchor agent exists in the network, and the other agents’ positions are estimated with respect to the anchor. We show that our centroid-based algorithm converges to the optimal solution, and such a centroid-based representation produces results that are more accurate than anchor-based ones, irrespective of the selected anchor.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2484597
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