Gibbs Sampler-Based Self-Organization of Autonomous Swarms

Gibbs Sampler-Based Self-Organization of Autonomous Swarms

Title : Gibbs Sampler-Based Self-Organization of Autonomous Swarms
Authors :
Xi, Wei
Tan, Xiaobo
Baras, John, S.
Conference : Automatica Vol. 42, Issue 7, pp. 1107-1119
Date: July 01 - July 01, 2006

In this paper a novel, Gibbs sampler-based algorithm is proposed for coordination of autonomous swarms. The swarm is modeled as a Markov random field (MRF) on a graph with a time-varying neighborhood system determined by local interaction links. The Gibbs potential is designed to reflect global objectives and constraints. It is established that with primarily local sensing/communications, the swarm configuration converges to the global minimizer(s) of the potential function. The impact of the Gibbs potential on the convergence speed is investigated. Finally, a hybrid algorithm is developed to improve the efficiency of the stochastic scheme by integrating the Gibbs sampler-based method with the deterministic gradient-flow method. Simulation results are presented to illustrate the proposed approach and verify the analyses.

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