In this paper, a novel distributed algorithm to deal with the problem of estimating the network centroid in a multi-agent system is proposed. In this scenario, agents are assumed to be lacking any global reference frame or absolute position information. The proposed algorithm can be thought as a general tool to retrieve information about the centroid of a network of agents. Indeed, this allows to release several simplifying assumptions for a significant family of algorithms dealing with decentralized motion coordination. The convergence properties of the algorithm are carefully investigated in the case of a fully connected network for which a proof of convergence is provided. Successively, simulations to show the effectiveness of the algorithm also for arbitrary undirected connected graphs are given.
Decentralized Centroid Estimation for Multi-Agent Systems in Absence of Any Global Reference Frame
FRANCESCHELLI, MAURO;
2009-01-01
Abstract
In this paper, a novel distributed algorithm to deal with the problem of estimating the network centroid in a multi-agent system is proposed. In this scenario, agents are assumed to be lacking any global reference frame or absolute position information. The proposed algorithm can be thought as a general tool to retrieve information about the centroid of a network of agents. Indeed, this allows to release several simplifying assumptions for a significant family of algorithms dealing with decentralized motion coordination. The convergence properties of the algorithm are carefully investigated in the case of a fully connected network for which a proof of convergence is provided. Successively, simulations to show the effectiveness of the algorithm also for arbitrary undirected connected graphs are given.
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