Gibbs sampler-based coordination of autonomous swarms

Authors:
Wei Xi;Xiaobo Tan;John S. Baras
Affiliations:
Institute for Systems Research, University of Maryland, College Park, MD 20742, USA and Department of Electrical & Computer Engineering, University of Maryland, College Park, MD 20742, USA;Department of Electrical & Computer Engineering, Michigan State University, East Lansing, MI 48824, USA;Institute for Systems Research, University of Maryland, College Park, MD 20742, USA and Department of Electrical & Computer Engineering, University of Maryland, College Park, MD 20742, USA
Venue:
Automatica (Journal of IFAC)
Year:
2006

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Brief paper: Decentralized coordination of autonomous swarms using parallel Gibbs sampling

Automatica (Journal of IFAC)

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Abstract

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.